├── LICENSE ├── PyDTI.py ├── README.md ├── blm.py ├── cmf.py ├── cv_eval.py ├── database ├── chembl.txt ├── chembl_uniprot_mapping.txt ├── drugbank.txt ├── kegg.txt ├── kegg_drug_map.txt ├── matador.tsv ├── pubchem_SIDs.txt ├── pubchem_SIDs_to_CIDs.txt ├── target_kegg_chembl.txt ├── target_kegg_uniprot.txt └── target_uniprot_chembl.txt ├── datasets ├── README ├── e_admat_dgc.txt ├── e_simmat_dc.txt ├── e_simmat_dg.txt ├── gpcr_admat_dgc.txt ├── gpcr_simmat_dc.txt ├── gpcr_simmat_dg.txt ├── ic_admat_dgc.txt ├── ic_simmat_dc.txt ├── ic_simmat_dg.txt ├── nr_admat_dgc.txt ├── nr_simmat_dc.txt └── nr_simmat_dg.txt ├── functions.py ├── kbmf.py ├── netlaprls.py ├── new_pairs.py ├── nrlmf.py ├── sat_analysis.py └── wnngip.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | 341 | -------------------------------------------------------------------------------- /PyDTI.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | import sys 4 | import time 5 | import getopt 6 | import cv_eval 7 | from functions import * 8 | from nrlmf import NRLMF 9 | from netlaprls import NetLapRLS 10 | from blm import BLMNII 11 | from wnngip import WNNGIP 12 | from kbmf import KBMF 13 | from cmf import CMF 14 | from new_pairs import novel_prediction_analysis 15 | 16 | 17 | def main(argv): 18 | try: 19 | opts, args = getopt.getopt(argv, "m:d:f:c:s:o:n:p", ["method=", "dataset=", "data-dir=", "cvs=", "specify-arg=", "method-options=", "predict-num=", "output-dir=", ]) 20 | except getopt.GetoptError: 21 | sys.exit() 22 | 23 | data_dir = os.path.join(os.path.pardir, 'data') 24 | output_dir = os.path.join(os.path.pardir, 'output') 25 | cvs, sp_arg, model_settings, predict_num = 1, 1, [], 0 26 | 27 | seeds = [7771, 8367, 22, 1812, 4659] 28 | # seeds = np.random.choice(10000, 5, replace=False) 29 | for opt, arg in opts: 30 | if opt == "--method": 31 | method = arg 32 | if opt == "--dataset": 33 | dataset = arg 34 | if opt == "--data-dir": 35 | data_dir = arg 36 | if opt == "--output-dir": 37 | output_dir = arg 38 | if opt == "--cvs": 39 | cvs = int(arg) 40 | if opt == "--specify-arg": 41 | sp_arg = int(arg) 42 | if opt == "--method-options": 43 | model_settings = [s.split('=') for s in str(arg).split()] 44 | if opt == "--predict-num": 45 | predict_num = int(arg) 46 | if not os.path.isdir(output_dir): 47 | os.makedirs(output_dir) 48 | 49 | # default parameters for each methods 50 | if method == 'nrlmf': 51 | args = {'c': 5, 'K1': 5, 'K2': 5, 'r': 50, 'lambda_d': 0.125, 'lambda_t': 0.125, 'alpha': 0.25, 'beta': 0.125, 'theta': 0.5, 'max_iter': 100} 52 | if method == 'netlaprls': 53 | args = {'gamma_d': 10, 'gamma_t': 10, 'beta_d': 1e-5, 'beta_t': 1e-5} 54 | if method == 'blmnii': 55 | args = {'alpha': 0.7, 'gamma': 1.0, 'sigma': 1.0, 'avg': False} 56 | if method == 'wnngip': 57 | args = {'T': 0.8, 'sigma': 1.0, 'alpha': 0.8} 58 | if method == 'kbmf': 59 | args = {'R': 50} 60 | if method == 'cmf': 61 | args = {'K': 50, 'lambda_l': 0.5, 'lambda_d': 0.125, 'lambda_t': 0.125, 'max_iter': 30} 62 | 63 | for key, val in model_settings: 64 | args[key] = val 65 | 66 | intMat, drugMat, targetMat = load_data_from_file(dataset, os.path.join(data_dir, 'datasets')) 67 | drug_names, target_names = get_drugs_targets_names(dataset, os.path.join(data_dir, 'datasets')) 68 | 69 | if predict_num == 0: 70 | if cvs == 1: # CV setting CVS1 71 | X, D, T, cv = intMat, drugMat, targetMat, 1 72 | if cvs == 2: # CV setting CVS2 73 | X, D, T, cv = intMat, drugMat, targetMat, 0 74 | if cvs == 3: # CV setting CVS3 75 | X, D, T, cv = intMat.T, targetMat, drugMat, 0 76 | cv_data = cross_validation(X, seeds, cv) 77 | 78 | if sp_arg == 0 and predict_num == 0: 79 | if method == 'nrlmf': 80 | cv_eval.nrlmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, args) 81 | if method == 'netlaprls': 82 | cv_eval.netlaprls_cv_eval(method, dataset, cv_data, X, D, T, cvs, args) 83 | if method == 'blmnii': 84 | cv_eval.blmnii_cv_eval(method, dataset, cv_data, X, D, T, cvs, args) 85 | if method == 'wnngip': 86 | cv_eval.wnngip_cv_eval(method, dataset, cv_data, X, D, T, cvs, args) 87 | if method == 'kbmf': 88 | cv_eval.kbmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, args) 89 | if method == 'cmf': 90 | cv_eval.cmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, args) 91 | 92 | if sp_arg == 1 or predict_num > 0: 93 | tic = time.clock() 94 | if method == 'nrlmf': 95 | model = NRLMF(cfix=args['c'], K1=args['K1'], K2=args['K2'], num_factors=args['r'], lambda_d=args['lambda_d'], lambda_t=args['lambda_t'], alpha=args['alpha'], beta=args['beta'], theta=args['theta'], max_iter=args['max_iter']) 96 | if method == 'netlaprls': 97 | model = NetLapRLS(gamma_d=args['gamma_d'], gamma_t=args['gamma_t'], beta_d=args['beta_t'], beta_t=args['beta_t']) 98 | if method == 'blmnii': 99 | model = BLMNII(alpha=args['alpha'], gamma=args['gamma'], sigma=args['sigma'], avg=args['avg']) 100 | if method == 'wnngip': 101 | model = WNNGIP(T=args['T'], sigma=args['sigma'], alpha=args['alpha']) 102 | if method == 'kbmf': 103 | model = KBMF(num_factors=args['R']) 104 | if method == 'cmf': 105 | model = CMF(K=args['K'], lambda_l=args['lambda_l'], lambda_d=args['lambda_d'], lambda_t=args['lambda_t'], max_iter=args['max_iter']) 106 | cmd = str(model) 107 | if predict_num == 0: 108 | print "Dataset:"+dataset+" CVS:"+str(cvs)+"\n"+cmd 109 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 110 | aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec) 111 | auc_avg, auc_conf = mean_confidence_interval(auc_vec) 112 | print "auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic) 113 | write_metric_vector_to_file(auc_vec, os.path.join(output_dir, method+"_auc_cvs"+str(cvs)+"_"+dataset+".txt")) 114 | write_metric_vector_to_file(aupr_vec, os.path.join(output_dir, method+"_aupr_cvs"+str(cvs)+"_"+dataset+".txt")) 115 | elif predict_num > 0: 116 | print "Dataset:"+dataset+"\n"+cmd 117 | seed = 7771 if method == 'cmf' else 22 118 | model.fix_model(intMat, intMat, drugMat, targetMat, seed) 119 | x, y = np.where(intMat == 0) 120 | scores = model.predict_scores(zip(x, y), 5) 121 | ii = np.argsort(scores)[::-1] 122 | predict_pairs = [(drug_names[x[i]], target_names[y[i]], scores[i]) for i in ii[:predict_num]] 123 | new_dti_file = os.path.join(output_dir, "_".join([method, dataset, "new_dti.txt"])) 124 | novel_prediction_analysis(predict_pairs, new_dti_file, os.path.join(data_dir, 'biodb')) 125 | 126 | if __name__ == "__main__": 127 | main(sys.argv[1:]) 128 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PyDTI -- a Python library for drug-target interaction prediction 2 | 3 | version 1.0, June 08 2015 4 | 5 | -------- 6 | This package is written by: 7 | 8 | Yong Liu 9 | 10 | Email: liuy0054@e.ntu.edu.sg 11 | 12 | School of Computer Engineering, Nanyang Technological University, Singapore 13 | Joint NTU-UBC Research Centre of Excellence in Active Living for Elderly (LILY), Nanyang Technological University, Singapore 14 | 15 | For any questions regarding to this library, please feel free to contact the author. 16 | 17 | -------- 18 | PyDTI is implemented by Python 2.7.9, which can be downloaded from: https://www.python.org/downloads/release/python-279/. PyDTI requires several other Python packages, including Numpy, scikit-learn, Scipy, and pymatbridge (a bridge between Python and Matlab). 19 | 20 | The original packages can be found here: 21 | http://www.numpy.org/ 22 | http://scikit-learn.org/stable/ 23 | http://www.scipy.org/ 24 | http://arokem.github.io/python-matlab-bridge/ 25 | 26 | Note that pymatbridge is only required by KBMF2K. The 64-bit Windows binaries of Numpy, scikit-learn, and Scipy can also be found at: http://www.lfd.uci.edu/~gohlke/pythonlibs/. 27 | 28 | 1. Add the folder "$PYTHON_ROOT$/Scipts/" to the system path. Please replace "$PYTHON_ROOT$" with the root folder of Python in your system. 29 | 30 | 2. Install the packages using pip utility. Open a console and type the following to install 31 | 32 | pip install numpy scipy scikit-learn 33 | 34 | -------- 35 | For the KBMF2K method, please download the matlab code implemented by M. Gonen from http://users.ics.aalto.fi/gonen/bioinfo12.php and put the matlab code in the sub-folder "kbmf2k/". To connect the Matlab code and Python, we need to define a Matlab function: 36 | 37 | function predictR = kbmf(args) 38 | Kx = args.Kx; 39 | Kz = args.Kz; 40 | Y = args.Y; 41 | R = args.R; 42 | state = kbmf_regression_train(Kx, Kz, Y, R); 43 | prediction = kbmf_regression_test(Kx, Kz, state); 44 | predictR = prediction.Y.mu; 45 | end 46 | 47 | Save this function into a Matlab file named kbmf.m and put this file into the subfolder "kbmf2k/". 48 | 49 | -------- 50 | To get the results of different methods, please run PyDTI.py by setting suitable values for the following parameters: 51 | 52 | --method set DTI prediction method 53 | --dataset: choose the benchmark dataset, i.e., nr, gpcr, ic, e 54 | --folder: set the the folder that contains the datasets (default "datasets/") 55 | --csv: choose the cross-validation setting, 1 for CVS1, 2 for CVS2, and 3 for CVS3, (default 1) 56 | --specify-arg: 0 for choosing optimal arguments, 1 for using default/specified arguments (default 1) 57 | --method-opt: set arguments for each method (method ARGUMENTS have the form name=value) 58 | --predict-num: 0 for not predicting novel DTIs, a positive integer for predicting top-N novel DTIs (default 0) 59 | 60 | Here are some examples: 61 | 62 | (1) run a method with default arguments 63 | python PyDTI.py --method="nrlmf" --dataset="nr" 64 | python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=2 65 | python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=2 --specify-arg=1 66 | 67 | (2) run a method with specified arguments 68 | 69 | python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=1 --specify-arg=1 --method-opt="r=100" 70 | 71 | python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=1 --specify-arg=1 --method-opt="c=5 K1=5 K2=5 r=100 lambda_d=0.125 lambda_t=0.125 alpha=0.25 beta=0.125 theta=0.5" 72 | 73 | You can refer to lines 47-58 in the PyDTI.py for the default parameters of each DTI prediction method. 74 | 75 | (3) choose the optimal parameters for a method 76 | 77 | python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=1 --specify-arg=0 78 | 79 | (4) predict the top-100 novel DTIs 80 | 81 | python PyDTI.py --method="nrlmf" --dataset="nr" --predict-num=100 --method-opt="r=100" 82 | 83 | 4. You can run sat_analysis.py for the statistical comparision between NRLMF and other baseline methods on all datasets, under different cross-validation settings. Note that you should first obtain the auc and aupr results of each method. 84 | 85 | python sta_analysis.py 86 | -------------------------------------------------------------------------------- /blm.py: -------------------------------------------------------------------------------- 1 | ''' 2 | [1] Mei, Jian-Ping, et al. "Drug target interaction prediction by learning from local information and neighbors." Bioinformatics 29.2 (2013): 238-245. 3 | [2] van Laarhoven, Twan, Sander B. Nabuurs, and Elena Marchiori. "Gaussian interaction profile kernels for predicting drug-target interaction." Bioinformatics 27.21 (2011): 3036-3043. 4 | 5 | Default Parameters: 6 | alpha = 0.5 7 | gamma = 1.0 (the gamma0 in [1], see Eq. 11 and 12 for details) 8 | avg = False (True: g=mean, False: g=max) 9 | sigma = 1.0 (The regularization parameter used for the RLS-avg classifier) 10 | Please refer to Section 4.1 in [1] and and Section 4 in [2] for the details. 11 | ''' 12 | import numpy as np 13 | from sklearn.metrics.pairwise import rbf_kernel 14 | from sklearn.metrics import precision_recall_curve, roc_curve 15 | from sklearn.metrics import auc 16 | 17 | 18 | class BLMNII: 19 | 20 | def __init__(self, alpha=0.5, gamma=1.0, sigma=1.0, avg=False): 21 | self.alpha = float(alpha) 22 | self.gamma = float(gamma) 23 | self.sigma = float(sigma) 24 | if avg in ('false', 'False', False): 25 | self.avg = False 26 | if avg in ('true', 'True', True): 27 | self.avg = True 28 | 29 | def kernel_combination(self, R, S, new_inx, bandwidth): 30 | K = self.alpha*S+(1.0-self.alpha)*rbf_kernel(R, gamma=bandwidth) 31 | K[new_inx, :] = S[new_inx, :] 32 | K[:, new_inx] = S[:, new_inx] 33 | return K 34 | 35 | def rls_train(self, R, S, K, train_inx, new_inx): 36 | Y = R.copy() 37 | for d in new_inx: 38 | Y[d, :] = np.dot(S[d, train_inx], Y[train_inx, :]) 39 | x1, x2 = np.max(Y[d, :]), np.min(Y[d, :]) 40 | Y[d, :] = (Y[d, :]-x2)/(x1-x2) 41 | vec = np.linalg.inv(K+self.sigma*np.eye(K.shape[0])) 42 | return np.dot(np.dot(K, vec), Y) 43 | 44 | def fix_model(self, W, intMat, drugMat, targetMat, seed=None): 45 | R = W*intMat 46 | m, n = intMat.shape 47 | x, y = np.where(R > 0) 48 | drugMat = (drugMat+drugMat.T)/2 49 | targetMat = (targetMat+targetMat.T)/2 50 | train_drugs = np.array(list(set(x.tolist())), dtype=np.int32) 51 | train_targets = np.array(list(set(y.tolist())), dtype=np.int32) 52 | new_drugs = np.array(list(set(xrange(m)) - set(x.tolist())), dtype=np.int32) 53 | new_targets = np.array(list(set(xrange(n)) - set(y.tolist())), dtype=np.int32) 54 | drug_bw = self.gamma*m/len(x) 55 | target_bw = self.gamma*n/len(x) 56 | 57 | Kd = self.kernel_combination(R, drugMat, new_drugs, drug_bw) 58 | Kt = self.kernel_combination(R.T, targetMat, new_targets, target_bw) 59 | self.Y1 = self.rls_train(R, drugMat, Kd, train_drugs, new_drugs) 60 | self.Y2 = self.rls_train(R.T, targetMat, Kt, train_targets, new_targets) 61 | 62 | def predict_scores(self, test_data, N): 63 | inx = np.array(test_data) 64 | x, y = inx[:, 0], inx[:, 1] 65 | if self.avg: 66 | scores = 0.5*(self.Y1[x, y]+self.Y2.T[x, y]) 67 | else: 68 | scores = np.maximum(self.Y1[x, y], self.Y2.T[x, y]) 69 | return scores 70 | 71 | def evaluation(self, test_data, test_label): 72 | x, y = test_data[:, 0], test_data[:, 1] 73 | if self.avg: 74 | scores = 0.5*(self.Y1[x, y]+self.Y2.T[x, y]) 75 | else: 76 | scores = np.maximum(self.Y1[x, y], self.Y2.T[x, y]) 77 | prec, rec, thr = precision_recall_curve(test_label, scores) 78 | aupr_val = auc(rec, prec) 79 | fpr, tpr, thr = roc_curve(test_label, scores) 80 | auc_val = auc(fpr, tpr) 81 | return aupr_val, auc_val 82 | 83 | def __str__(self): 84 | return "Model:BLMNII, alpha:%s, gamma:%s, sigma:%s, avg:%s" % (self.alpha, self.gamma, self.sigma, self.avg) 85 | 86 | if __name__ == "__main__": 87 | import time 88 | from functions import * 89 | seeds = [7771, 8367, 22, 1812, 4659] 90 | for cv_setting in [1, 2, 3]: 91 | for dataset in ["nr", "gpcr", "ic", "e"]: 92 | intMat, drugMat, targetMat = load_data_from_file(dataset, "../dataset/") 93 | if cv_setting == 1: # CV setting S1 94 | X, D, T, cv = intMat, drugMat, targetMat, 1 95 | if cv_setting == 2: # CV setting S2 96 | X, D, T, cv = intMat, drugMat, targetMat, 0 97 | if cv_setting == 3: # CV setting S3 98 | X, D, T, cv = intMat.T, targetMat, drugMat, 0 99 | max_auc, max_aupr, auc_opt, aupr_opt = 0, 0, [], [] 100 | for x in np.arange(0, 0.1, 0.1): 101 | cv_data = cross_validation(X, seeds, cv) 102 | tic = time.clock() 103 | model = BLMNII(alpha=x, avg=False) 104 | cmd = "dataset:"+dataset+", cross_validation: "+str(cv_setting)+"\n"+str(model) 105 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 106 | # aupr_avg, auc_avg = np.mean(aupr_vec), np.mean(auc_vec) 107 | aupr_avg, aupr_st = mean_confidence_interval(aupr_vec) 108 | auc_avg, auc_st = mean_confidence_interval(auc_vec) 109 | print cmd 110 | print "AUPR: %s, AUC:%s, AUPRst:%s, AUCst:%s, Time:%s" % (aupr_avg, auc_avg, aupr_st, auc_st, time.clock() - tic) 111 | if aupr_avg > max_aupr: 112 | max_aupr = aupr_avg 113 | aupr_opt = [cmd, aupr_avg, auc_avg] 114 | if auc_avg > max_auc: 115 | max_auc = auc_avg 116 | auc_opt = [cmd, aupr_avg, auc_avg] 117 | # cmd = "Optimal Parameters for AUPR optimization:\n%s\n" % aupr_opt[0] 118 | # cmd += "AUPR: %s, AUC: %s\n" % (aupr_opt[1], aupr_opt[2]) 119 | # cmd += "Optimal Parameters for AUC optimization:\n%s\n" % auc_opt[0] 120 | # cmd += "AUPR: %s, AUC: %s" % (auc_opt[1], auc_opt[2]) 121 | # print "\n"+cmd 122 | # with open("../output/blmnii_results.txt", "a+") as outf: 123 | # outf.write("Dataset:"+dataset+"\n"+cmd+"\n\n") 124 | write_metric_vector_to_file(aupr_vec, "../output/blm_aupr_"+str(cv_setting)+"_"+dataset+".txt") 125 | write_metric_vector_to_file(auc_vec, "../output/blm_auc_"+str(cv_setting)+"_"+dataset+".txt") 126 | -------------------------------------------------------------------------------- /cmf.py: -------------------------------------------------------------------------------- 1 | 2 | ''' 3 | [1] X. Zheng, H. Ding, H. Mamitsuka, and S. Zhu, "Collaborative matrix factorization with multiple similarities for predicting drug-target interaction", KDD, 2013. 4 | 5 | ''' 6 | import numpy as np 7 | from sklearn.metrics import precision_recall_curve, roc_curve 8 | from sklearn.metrics import auc 9 | 10 | 11 | class CMF: 12 | 13 | def __init__(self, K=10, lambda_l=0.01, lambda_d=0.01, lambda_t=0.01, max_iter=100): 14 | self.K = K 15 | self.lambda_l = lambda_l 16 | self.lambda_d = lambda_d 17 | self.lambda_t = lambda_t 18 | self.max_iter = max_iter 19 | 20 | def fix_model(self, W, intMat, drugMat, targetMat, seed): 21 | self.num_drugs, self.num_targets = intMat.shape 22 | self.drugMat, self.targetMat = drugMat, targetMat 23 | x, y = np.where(W > 0) 24 | self.train_drugs = set(x.tolist()) 25 | self.train_targets = set(y.tolist()) 26 | if seed is None: 27 | self.U = np.sqrt(1/float(self.K))*np.random.normal(size=(self.num_drugs, self.K)) 28 | self.V = np.sqrt(1/float(self.K))*np.random.normal(size=(self.num_targets, self.K)) 29 | else: 30 | prng = np.random.RandomState(seed) 31 | self.U = np.sqrt(1/float(self.K))*prng.normal(size=(self.num_drugs, self.K)) 32 | self.V = np.sqrt(1/float(self.K))*prng.normal(size=(self.num_targets, self.K)) 33 | self.ones = np.identity(self.K) 34 | last_loss = self.compute_loss(W, intMat, drugMat, targetMat) 35 | WR = W*intMat 36 | for t in xrange(self.max_iter): 37 | self.U = self.als_update(self.U, self.V, W, WR, drugMat, self.lambda_l, self.lambda_d) 38 | self.V = self.als_update(self.V, self.U, W.T, WR.T, targetMat, self.lambda_l, self.lambda_t) 39 | curr_loss = self.compute_loss(W, intMat, drugMat, targetMat) 40 | delta_loss = (curr_loss-last_loss)/last_loss 41 | # print "Epoach:%s, Curr_loss:%s, Delta_loss:%s" % (t+1, curr_loss, delta_loss) 42 | if abs(delta_loss) < 1e-6: 43 | break 44 | last_loss = curr_loss 45 | 46 | def als_update(self, U, V, W, R, S, lambda_l, lambda_d): 47 | X = R.dot(V) + 2*lambda_d*S.dot(U) 48 | Y = 2*lambda_d*np.dot(U.T, U) 49 | Z = lambda_d*(np.diag(S)-np.sum(np.square(U), axis=1)) 50 | U0 = np.zeros(U.shape) 51 | D = np.dot(V.T, V) 52 | m, n = W.shape 53 | for i in xrange(m): 54 | # A = np.dot(V.T, np.diag(W[i, :])) 55 | # B = A.dot(V) + Y + (lambda_l+Z[i])*self.ones 56 | ii = np.where(W[i, :] > 0)[0] 57 | if ii.size == 0: 58 | B = Y + (lambda_l+Z[i])*self.ones 59 | elif ii.size == n: 60 | B = D + Y + (lambda_l+Z[i])*self.ones 61 | else: 62 | A = np.dot(V[ii, :].T, V[ii, :]) 63 | B = A + Y + (lambda_l+Z[i])*self.ones 64 | U0[i, :] = X[i, :].dot(np.linalg.inv(B)) 65 | return U0 66 | 67 | def compute_loss(self, W, intMat, drugMat, targetMat): 68 | loss = np.linalg.norm(W * (intMat - np.dot(self.U, self.V.T)), "fro")**(2) 69 | loss += self.lambda_l*(np.linalg.norm(self.U, "fro")**(2)+np.linalg.norm(self.V, "fro")**(2)) 70 | loss += self.lambda_d*np.linalg.norm(drugMat-self.U.dot(self.U.T), "fro")**(2)+self.lambda_t*np.linalg.norm(targetMat-self.V.dot(self.V.T), "fro")**(2) 71 | return 0.5*loss 72 | 73 | def evaluation(self, test_data, test_label): 74 | ii, jj = test_data[:, 0], test_data[:, 1] 75 | scores = np.sum(self.U[ii, :]*self.V[jj, :], axis=1) 76 | prec, rec, thr = precision_recall_curve(test_label, scores) 77 | aupr_val = auc(rec, prec) 78 | fpr, tpr, thr = roc_curve(test_label, scores) 79 | auc_val = auc(fpr, tpr) 80 | return aupr_val, auc_val 81 | 82 | def predict_scores(self, test_data, N): 83 | inx = np.array(test_data) 84 | return np.sum(self.U[inx[:, 0], :]*self.V[inx[:, 1], :], axis=1) 85 | 86 | def __str__(self): 87 | return "Model: CMF, K:%s, lambda_l:%s, lambda_d:%s, lambda_t:%s, max_iter:%s" % (self.K, self.lambda_l, self.lambda_d, self.lambda_t, self.max_iter) 88 | -------------------------------------------------------------------------------- /cv_eval.py: -------------------------------------------------------------------------------- 1 | 2 | import time 3 | from functions import * 4 | from nrlmf import NRLMF 5 | from netlaprls import NetLapRLS 6 | from blm import BLMNII 7 | from wnngip import WNNGIP 8 | from kbmf import KBMF 9 | from cmf import CMF 10 | 11 | 12 | def nrlmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, para): 13 | max_auc, auc_opt = 0, [] 14 | for r in [50, 100]: 15 | for x in np.arange(-5, 2): 16 | for y in np.arange(-5, 3): 17 | for z in np.arange(-5, 1): 18 | for t in np.arange(-3, 1): 19 | tic = time.clock() 20 | model = NRLMF(cfix=para['c'], K1=para['K1'], K2=para['K2'], num_factors=r, lambda_d=2**(x), lambda_t=2**(x), alpha=2**(y), beta=2**(z), theta=2**(t), max_iter=100) 21 | cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model) 22 | print cmd 23 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 24 | aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec) 25 | auc_avg, auc_conf = mean_confidence_interval(auc_vec) 26 | print "auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic) 27 | if auc_avg > max_auc: 28 | max_auc = auc_avg 29 | auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf] 30 | cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0] 31 | cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4]) 32 | print cmd 33 | 34 | 35 | def netlaprls_cv_eval(method, dataset, cv_data, X, D, T, cvs, para): 36 | max_auc, auc_opt = 0, [] 37 | for x in np.arange(-6, 3): # [-6, 2] 38 | for y in np.arange(-6, 3): # [-6, 2] 39 | tic = time.clock() 40 | model = NetLapRLS(gamma_d=10**(x), gamma_t=10**(x), beta_d=10**(y), beta_t=10**(y)) 41 | cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model) 42 | print cmd 43 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 44 | aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec) 45 | auc_avg, auc_conf = mean_confidence_interval(auc_vec) 46 | print "auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic) 47 | if auc_avg > max_auc: 48 | max_auc = auc_avg 49 | auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf] 50 | cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0] 51 | cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4]) 52 | print cmd 53 | 54 | 55 | def blmnii_cv_eval(method, dataset, cv_data, X, D, T, cvs, para): 56 | max_auc, auc_opt = 0, [] 57 | for x in np.arange(0, 1.1, 0.1): 58 | tic = time.clock() 59 | model = BLMNII(alpha=x, avg=False) 60 | cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model) 61 | print cmd 62 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 63 | aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec) 64 | auc_avg, auc_conf = mean_confidence_interval(auc_vec) 65 | print "auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic) 66 | if auc_avg > max_auc: 67 | max_auc = auc_avg 68 | auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf] 69 | cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0] 70 | cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4]) 71 | print cmd 72 | 73 | 74 | def wnngip_cv_eval(method, dataset, cv_data, X, D, T, cvs, para): 75 | max_auc, auc_opt = 0, [] 76 | for x in np.arange(0.1, 1.1, 0.1): 77 | for y in np.arange(0.0, 1.1, 0.1): 78 | tic = time.clock() 79 | model = WNNGIP(T=x, sigma=1, alpha=y) 80 | cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model) 81 | print cmd 82 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 83 | aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec) 84 | auc_avg, auc_conf = mean_confidence_interval(auc_vec) 85 | print "auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic) 86 | if auc_avg > max_auc: 87 | max_auc = auc_avg 88 | auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf] 89 | cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0] 90 | cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4]) 91 | print cmd 92 | 93 | 94 | def kbmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, para): 95 | max_auc, auc_opt = 0, [] 96 | for d in [50, 100]: 97 | tic = time.clock() 98 | model = KBMF(num_factors=d) 99 | cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model) 100 | print cmd 101 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 102 | aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec) 103 | auc_avg, auc_conf = mean_confidence_interval(auc_vec) 104 | print "auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic) 105 | if auc_avg > max_auc: 106 | max_auc = auc_avg 107 | auc_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf] 108 | cmd = "Optimal parameter setting:\n%s\n" % auc_opt[0] 109 | cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (auc_opt[1], auc_opt[2], auc_opt[3], auc_opt[4]) 110 | print cmd 111 | 112 | 113 | def cmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, para): 114 | max_aupr, aupr_opt = 0, [] 115 | for d in [50, 100]: 116 | for x in np.arange(-2, -1): 117 | for y in np.arange(-3, -2): 118 | for z in np.arange(-3, -2): 119 | tic = time.clock() 120 | model = CMF(K=d, lambda_l=2**(x), lambda_d=2**(y), lambda_t=2**(z), max_iter=30) 121 | cmd = "Dataset:"+dataset+" CVS: "+str(cvs)+"\n"+str(model) 122 | print cmd 123 | aupr_vec, auc_vec = train(model, cv_data, X, D, T) 124 | aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec) 125 | auc_avg, auc_conf = mean_confidence_interval(auc_vec) 126 | print "auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f\n" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic) 127 | if aupr_avg > max_aupr: 128 | max_aupr = aupr_avg 129 | aupr_opt = [cmd, auc_avg, aupr_avg, auc_conf, aupr_conf] 130 | cmd = "Optimal parameter setting:\n%s\n" % aupr_opt[0] 131 | cmd += "auc: %.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f\n" % (aupr_opt[1], aupr_opt[2], aupr_opt[3], aupr_opt[4]) 132 | print cmd 133 | -------------------------------------------------------------------------------- /database/drugbank.txt: -------------------------------------------------------------------------------- 1 | DB00777 P08172 P08173 P28223 P20309 P28335 P08912 P25100 P21728 P14416 P35368 P35367 P35348 P21917 P11229 2 | DB00776 Q14524 3 | DB00548 P51857 P66010 P14679 P31213 P00582 4 | DB01098 P04035 5 | DB01095 P04035 6 | DB01097 P35869 Q14289 Q02127 7 | DB00770 P34995 P43116 8 | DB01259 P00533 P04626 9 | DB01258 10 | DB01252 Q09428 P37231 11 | DB01076 P35869 P27487 P04035 12 | DB01075 P35367 13 | DB01073 P09884 P27707 P23921 14 | DB01072 Q72874 15 | DB01071 P35367 16 | DB01070 P11473 17 | DB01176 P35367 P49888 18 | DB01177 P11388 19 | DB01174 Q05586 Q13002 O15399 O60391 Q13224 Q12879 Q8TCU5 P14867 P36544 Q14957 P43681 P42262 20 | DB01079 P28223 P28335 P41595 Q13639 21 | DB01171 P21397 22 | DB00779 23 | DB00470 P34972 P21554 24 | DB00472 P31645 25 | DB00474 P14867 26 | DB00477 P19652 P18825 P02763 P28223 P35462 P11229 Q9H3N8 Q12809 P21728 P08913 P34969 P14416 P28335 P41595 P21918 P18089 P35368 P35367 P35348 P21917 P17405 P62158 P25100 P50406 P20309 P08908 27 | DB01002 Q9Y5Y9 28 | DB01001 P07550 P08588 29 | DB01006 P11511 30 | DB01004 P04293 P03176 31 | DB00571 P07550 P28222 P08588 P13945 P08908 32 | DB00573 P35354 P23219 33 | DB00572 P08172 P08173 P08912 P11229 P20309 34 | DB00575 P18825 P25100 P08913 P18089 P35368 P35348 35 | DB00577 P04293 Q9QNF7 36 | DB00751 P28223 P34969 P25021 P08913 P35367 P35348 37 | DB00750 Q14524 38 | DB00753 P62158 P98194 Q09470 P47869 Q16445 P78334 P18507 P18505 Q8N1C3 P14867 P28472 O00591 Q9UN88 P23415 P30049 O14764 P48169 P34903 P47870 P31644 Q99928 P42261 39 | DB00752 P21397 P27338 40 | DB00755 P00352 O94788 P51843 P48443 P28702 Q8NFJ5 P13631 P49788 41 | DB00754 Q14524 42 | DB00209 P11229 43 | DB00758 Q9H244 44 | DB00977 O75469 P03372 45 | DB00204 O95069 Q12809 Q14500 46 | DB00203 P18545 Q13956 O76074 47 | DB00201 Q01064 Q14432 Q9HCR9 Q9NP56 Q08493 O95263 P21817 P16499 Q08499 P78527 Q13946 O76083 Q9Y233 Q13315 Q13370 P35913 P29274 P30542 O60658 P54750 P42338 P42336 Q14643 O76074 O00329 Q14123 P27815 O00408 Q14571 Q14573 P51160 Q07343 48 | DB00617 Q9P0X4 49 | DB00683 Q9UN88 P24046 P28472 P18507 P18505 O00591 Q16445 A8MPY1 O14764 P48169 P47870 P28476 Q8N1C3 P31644 P14867 P47869 P34903 Q99928 P78334 50 | DB00681 51 | DB00680 Q14524 52 | DB00619 P00519 P07333 A9UF02 O43519 P16234 Q08345 P09619 P04629 P10721 53 | DB04272 P32427 Q9X0N8 Q9BY32 P03950 Q02834 P04190 P32173 Q9HU22 P14174 P06672 P15121 P38489 P15086 O33833 Q2QJL3 Q9GV41 P05042 Q9BZX2 P33644 P00808 P44725 Q9Y663 Q93088 O34002 P46849 P07741 Q9Y275 P11922 P07998 Q14749 P16608 Q7CL96 P39126 O14964 Q9WZW1 P21408 O75390 P64170 P13299 P0A753 O76074 P12931 P03277 Q81F54 P10930 P00953 Q9HB21 P0A955 P04079 P0A590 P12724 Q9LCX9 P62312 Q96RQ9 Q9WZ40 Q37875 Q9CBQ2 Q04095 Q9X286 P38516 P45568 Q9X1F0 P40926 P07360 Q9GZU7 P83787 54 | DB00688 P20839 P12268 55 | DB00372 P21728 P21917 P14416 56 | DB01677 P23368 P0C278 P80040 P83223 P16930 57 | DB00370 P18825 P28223 P08913 P35462 P46098 P34969 Q01959 P21728 P08588 P14416 P23975 Q9Y5N1 P35368 P41145 P28335 P41595 P21918 P31645 P35367 P35348 P25100 P07550 58 | DB00273 P00918 P22748 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 Q9UN88 Q8N1C3 P47869 P14867 P28472 P35498 P39086 P34903 Q99928 P31644 P78334 59 | DB00270 O95180 P54289 Q08289 Q13936 Q13698 Q01668 Q9NY47 60 | DB00378 P06401 61 | DB00276 P11388 Q12809 62 | DB00277 P27815 Q14432 O76074 Q92769 Q07343 P29274 P29275 P30542 63 | DB00104 P30874 P35346 P30872 64 | DB00961 Q9Y5Y9 65 | DB00296 Q9Y5Y9 66 | DB00297 P34995 Q9Y5Y9 67 | DB00292 Q9UN88 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 P18089 Q8N1C3 P31644 P14867 P28472 P47869 P34903 Q99928 P78334 68 | DB00293 Q05932 P04818 69 | DB00741 P04150 P04083 70 | DB00299 P06478 P04293 71 | DB00198 P11485 Q9Y3R4 Q99519 P23141 72 | DB00196 P10613 73 | DB00195 P07550 P08588 74 | DB00191 P21397 P31645 P23975 Q01959 P27338 75 | DB01283 P35354 P23219 76 | DB00997 P11388 77 | DB00996 Q05586 O15399 Q8TCU5 O60391 P54289 Q13224 Q12879 Q00975 Q14957 Q9NY47 P30542 78 | DB01217 P11511 79 | DB00991 P35354 P23219 80 | DB01214 P07550 P08588 81 | DB00857 Q14534 82 | DB01219 P21817 83 | DB01218 Q12809 P46925 84 | DB00252 Q14524 P35498 85 | DB00850 P21728 P62158 P14416 86 | DB01355 Q16445 P48169 P31644 P14867 Q13002 P36544 P47869 P34903 P43681 P42262 87 | DB00496 P08172 P08173 P08912 P11229 P20309 88 | DB01039 O43923 Q07869 89 | DB01032 Q4U2R8 Q8TCC7 Q9NSA0 Q96RD7 90 | DB01031 P00918 P00915 91 | DB01030 Q969P6 P11387 92 | DB01037 P21397 P27338 93 | DB01036 P08172 P08173 P08912 P11229 P20309 94 | DB01035 P26358 Q14524 95 | DB00323 P21964 96 | DB01202 Q00975 Q7L0J3 97 | DB00537 P11388 P43700 P43702 98 | DB00530 P00533 O75469 99 | DB00533 P35354 P15502 100 | DB01144 P00918 P22748 P00915 P43166 101 | DB00328 Q8N8N7 Q07869 P35354 Q04760 P37231 Q9Y5Y4 P14555 P23219 102 | DB01628 P35354 103 | DB00499 P10275 P35869 104 | DB00324 P04150 105 | DB00497 P41145 P35372 P19652 P02763 P41143 106 | DB00494 P21964 107 | DB00492 P12821 108 | DB00321 P08173 P28221 P28222 P28223 P35372 P07550 P04629 O43525 O43526 P11229 Q09470 Q9H3N8 Q99720 P34969 P08912 P08913 P23975 P08172 P41145 Q16620 Q9NZV8 P41143 P28335 P08588 P35368 P35367 Q9UK17 P35348 P13945 P25021 P25100 P50406 P20309 P08908 P31645 109 | DB00322 P04818 110 | DB00491 P10253 Q14697 O43451 Q8TET4 111 | DB00611 P41145 P35372 P41143 112 | DB02262 P05020 Q53ZE5 P08870 P0A7E1 Q02127 113 | DB01106 O95665 P35367 114 | DB00408 P18825 P28221 P28222 P28223 Q01959 P35462 P46098 P47898 P28566 P11229 Q9H3N8 P34969 P08912 P21728 P08913 P14416 P23975 P08172 P08173 P28335 P18089 P08588 P35368 P35367 P35348 P21917 P21918 P25021 P50406 P20309 P08908 P31645 115 | DB01131 P00374 P13922 116 | DB01136 P17302 P16581 Q16665 P18825 P08913 Q12809 P25100 P08588 P18089 P19320 O95298 P07550 P15692 P35348 P35368 P16860 117 | DB01135 P08172 Q15822 118 | DB00401 Q13698 Q01668 Q13936 P54289 Q08289 119 | DB01138 P33527 Q92887 120 | DB00404 Q9UN88 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 Q8N1C3 P31644 P14867 P28472 P47869 P34903 Q99928 P78334 121 | DB00926 P19793 P28702 P48443 P10276 P10826 P13631 122 | DB00927 P25021 123 | DB00665 P10275 124 | DB00920 O60658 Q13946 P27815 Q08493 P52209 Q9NP56 Q07343 O95263 P35367 Q08499 125 | DB00669 P30939 P28221 P28222 P08908 126 | DB00668 P00439 P18825 P25100 P18089 P08588 P08913 P01375 P35368 P07550 P35348 P13945 127 | DB00142 O43424 P07814 Q9H1K4 Q5TDP6 P24298 Q05932 O15382 Q05329 Q5JPH6 P80404 Q9UPY5 P48507 P48506 O14841 Q12879 Q9H936 P54886 P08243 Q99259 Q8N5Z0 P49448 O15399 Q13255 Q04609 Q9Y646 O15067 P48664 P48058 Q05586 P43004 P39086 P49915 Q6YP21 Q8N159 O94925 O00222 Q13003 Q13002 Q9UDR5 O00341 Q07075 O95954 O60391 Q8TD30 Q6IA69 Q9UI32 Q9ULK0 Q14957 Q14831 Q14833 P38435 P17174 Q9ULA0 P17735 P54687 Q2TU84 P00367 Q13224 P00505 Q16478 P43003 Q8TCU5 Q16099 P43005 P15104 Q9Y617 O75879 P42261 P42262 P42263 128 | DB00143 Q7RTV2 Q9H4Y5 O95881 P30711 Q04760 Q9Y2Q3 Q16873 P09211 O75715 P22352 P08263 P35754 O60760 Q8TED1 P28161 Q16772 Q16775 P09210 O15217 P00390 P46439 P78417 P10768 P10620 P36969 Q96SL4 P48637 P19440 P59796 O14880 P18283 P09488 P21266 P07203 Q99735 Q6NSD4 Q03013 Q9NS18 O43708 129 | DB00881 P12821 130 | DB00880 P55017 P00918 P00915 P22748 131 | DB00883 P16066 132 | DB00929 P43115 P35408 P43116 133 | DB00390 P05023 134 | DB00393 P54284 P08235 P35869 Q08289 Q13936 Q02641 O00305 Q13698 O60840 Q01668 135 | DB00392 Q8TCU5 P08172 P11229 136 | DB01170 P23975 137 | DB00396 P06401 P41145 P05093 P08235 P03372 138 | DB00399 P14324 O95749 139 | DB01078 P05023 140 | DB00347 O43497 141 | DB00715 P08172 P08173 P11229 P28223 P08912 P20309 P31645 P23975 142 | DB00242 Q07864 P00491 Q9NR33 P56282 Q7LG56 P09884 P23921 P31350 Q9NRF9 143 | DB00717 P06401 144 | DB00716 Q13258 Q9Y271 P07900 P21462 Q9NS75 145 | DB00247 P28566 P28335 P28222 P28223 P41595 P30939 P34969 P08908 146 | DB03849 Q07343 Q08499 147 | DB00712 P35354 P23219 148 | DB00249 Q9QNF7 149 | DB00248 P28335 P35462 P18825 P28221 P28222 P28223 P25100 P18089 P34969 P21918 P21728 P08913 P35368 P41595 P14416 P08908 P07550 P08588 P35348 P21917 150 | DB01231 P08172 P11229 P20309 151 | DB00812 P35354 Q16647 P23219 152 | DB00810 P11229 Q15822 153 | DB00811 P20839 P22413 P49902 P55263 P16502 P26676 154 | DB00814 P35354 P23219 155 | DB00818 Q9UN88 P28472 P18507 P18505 O00591 Q16445 O14764 P48169 P14867 Q8N1C3 P31644 P47870 P35499 Q99250 P47869 P34903 Q99928 P78334 156 | DB00819 P00918 P22748 P07451 P43166 Q9ULX7 P00915 P29972 157 | DB01010 P22303 P06276 158 | DB01209 P41145 P35372 159 | DB03147 Q9WYT0 P21397 P00387 P10902 O06934 Q9KIE5 P20586 P37747 P28593 P77967 P16640 P40859 P18925 P61432 Q56839 P05327 Q15067 P08373 P06715 Q51225 P09622 Q52437 P16219 Q9Z4P0 Q9RA47 P38038 P55792 P00438 P16435 P58558 P24232 P27338 P0C278 Q06319 P15559 Q9UKU7 P0A9P4 P83223 P42593 P12676 O95831 Q888A4 P21890 Q9UHQ9 P14920 P00390 Q12882 P37062 P29475 O31616 P37063 P09063 Q47PU3 Q92947 P22570 O05783 P35340 P22637 Q7SID9 P06149 P00914 P09788 P47989 Q9RC23 P14218 Q72JJ3 Q9AGP8 P55789 Q44532 P16083 P28861 P11310 Q9LAG2 P0AEZ1 Q96RQ9 P07771 Q94655 Q16881 P39662 P19480 P09546 P61497 Q86YB8 O53355 P26440 160 | DB00403 P32238 161 | DB00867 P07550 162 | DB01088 P43119 P27815 P34995 Q08493 P00750 Q07343 Q08499 163 | DB00864 P62942 164 | DB01367 P10415 P27338 165 | DB00862 P18545 Q13956 O76074 166 | DB00861 P35354 P23219 167 | DB01082 P0A7S3 168 | DB01083 P16233 P49327 P07098 169 | DB01080 Q9UBS5 P80404 170 | DB01086 Q9Y5Y9 171 | DB00869 P00918 P22748 P00915 172 | DB01085 P08172 P11229 P20309 173 | DB01244 P54750 O95180 P05023 O00555 P51787 P62158 Q01064 P63316 Q9NY47 174 | DB01247 P21397 P27338 175 | DB01240 P43119 Q16647 Q9H244 176 | DB01064 P28482 P27986 Q92569 P08588 Q4ZHU6 P07550 O00459 P13945 177 | DB00661 P54284 Q14524 Q00975 Q12809 O00555 Q14654 Q08289 Q9P0X4 Q13936 Q02641 O00305 O43497 P31645 Q13698 O60840 Q01668 178 | DB01108 Q92731 P26439 P14060 P03372 179 | DB01062 P08172 P11229 P20309 180 | DB00593 O43497 181 | DB01101 P04818 182 | DB01100 Q12809 P62158 P35462 P14416 183 | DB01069 P08172 P08173 P11229 P20309 P28223 P62158 P08912 P14416 P35367 P35348 184 | DB01104 P31645 Q01959 185 | DB00889 P46098 186 | DB03783 P23219 187 | DB00579 P31645 P23975 Q01959 188 | DB00564 Q14524 189 | DB00565 Q15822 190 | DB00449 P07550 P22303 P08913 P35348 P06276 191 | DB00563 P00374 192 | DB00445 O14646 P11388 193 | DB00444 P11388 194 | DB00441 P30085 P04818 P23921 195 | DB00440 P00374 P04818 196 | DB00568 P08172 P08173 O95180 P14416 P21918 P20309 P08912 Q9P0X4 Q13698 P21728 Q13936 O43497 P35367 O60840 Q01668 P11229 197 | DB00569 P01008 P00742 198 | DB00140 P0AFU8 P30043 Q969G6 199 | DB04224 P29498 Q9NZD2 P02689 200 | DB00749 P35354 P19793 P23219 201 | DB00746 202 | DB00960 P07550 P28222 P08588 P08908 203 | DB00217 P18825 P48048 P18089 P08588 P08913 P07550 P13945 204 | DB00966 P30556 P37231 205 | DB00740 Q14524 Q9UPY5 206 | DB00213 P20648 207 | DB00882 P03372 208 | DB00690 Q9UN88 P24046 Q16445 P18507 P18505 A8MPY1 O00591 O14764 P48169 P47870 P28476 Q8N1C3 P31644 P14867 P28472 P47869 P34903 Q99928 P78334 209 | DB00691 Q9BYF1 P12821 210 | DB00692 P08913 P35348 211 | DB01009 P25024 P35354 P23219 212 | DB00694 P11388 Q02880 213 | DB00695 P00918 Q13621 214 | DB02789 O00204 215 | DB00363 P18825 P28221 P28222 P28223 P35462 P46098 P28566 P11229 Q9H3N8 P34969 P08912 P21728 P08913 P14416 P08172 P08173 P28335 P18089 P35368 P35367 Q9NYX4 P35348 P21917 P50406 P20309 P08908 216 | DB00366 P35367 P11229 217 | DB00367 P10275 P06401 P18405 P03372 218 | DB00131 P09467 Q9Y478 Q08828 P16220 O43741 P11309 Q9NUB1 P49773 P55263 P06737 Q07343 Q9NR19 Q08499 Q13131 P33121 219 | DB00134 Q9UBK8 Q93088 P50579 Q99707 Q9H2M3 220 | DB00136 P11473 221 | DB00629 P08913 P18089 222 | DB04540 P35398 223 | DB00917 P34995 P43115 P35408 P43116 224 | DB00912 Q09428 P37231 225 | DB00910 P11473 226 | DB00624 P10275 227 | DB00627 Q15274 P49019 Q8TDS4 P40261 228 | DB00621 P10275 229 | DB00189 Q9UN88 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 Q8N1C3 P31644 P14867 P28472 P47869 P34903 Q99928 P78334 230 | DB00623 P21728 P62158 P14416 231 | DB00622 P08172 P08173 P54750 P11229 Q01064 P62158 P54289 Q08289 P08912 Q13936 P25100 P20309 P35368 Q01668 P35348 232 | DB00184 Q15825 P32297 P28329 Q15822 P30926 Q9UGM1 Q9GZZ6 P30532 Q05901 P36544 P17787 P43681 P11511 233 | DB00180 P04150 234 | DB00612 P07550 P08588 235 | DB00973 P35610 P15144 Q9UHC9 236 | DB00200 O60494 P22033 Q99707 Q8IVH4 Q9Y4U1 P20061 Q9UBK8 Q96EY8 Q9BXJ7 237 | DB01200 P28335 P14416 P18825 P28221 P28222 P28223 P25100 P18089 P34969 P21918 P21728 P08913 P35368 P41595 P35462 P08908 P35348 P21917 238 | DB00985 P35367 239 | DB00829 Q9UN88 P24046 P28472 P18507 P18505 A8MPY1 O00591 O14764 P48169 P47870 P28476 Q8N1C3 P31644 P14867 P30536 Q16445 P47869 P34903 Q99928 P78334 240 | DB00987 P06746 241 | DB01204 P11388 242 | DB01205 Q9UN88 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 Q8N1C3 P47869 P14867 P28472 P31644 P34903 Q99928 P78334 243 | DB00982 P10276 244 | DB00823 P06401 P03372 245 | DB00822 P05091 P09172 246 | DB00820 Q9HCR9 O76074 247 | DB00988 P14416 P34969 Q01959 P21918 P21728 P35462 P08908 P31645 P23975 P21917 P09172 248 | DB00826 249 | DB00824 P27815 P33765 Q07343 P29274 P29275 P30542 250 | DB01028 P98194 Q09470 P47869 Q16445 P78334 P18507 P18505 Q8N1C3 P14867 P28472 O00591 Q9UN88 P23415 P30049 O14764 P48169 P34903 P47870 P31644 P03886 Q99928 P42261 251 | DB01029 P05412 P30556 252 | DB01020 P33402 253 | DB01021 P05023 P00918 Q13621 P00915 P22748 254 | DB01022 P38435 P02818 255 | DB01023 P02585 P54750 O95180 Q01064 P62158 P54289 Q08289 P63316 Q13936 Q13698 P08235 Q01668 Q9NY47 256 | DB01025 P08700 Q99584 P05230 P80511 257 | DB01026 P10613 P11511 P08686 P10275 258 | DB00529 P08546 P04293 259 | DB00522 P05981 260 | DB00527 P62158 Q14524 Q9Y5Y9 261 | DB01154 Q15842 P14867 Q14654 262 | DB00318 P41145 P35372 P41143 263 | DB00091 P62937 P30405 P49069 Q96LZ3 264 | DB01151 P18825 P28223 P08913 P11229 P08912 O67854 P14416 P23975 P35368 P08172 P08173 P28335 P18089 P08588 P31645 P35367 P35348 P17405 P25100 P20309 P08908 P07550 265 | DB00311 P00918 P22748 P00915 P43166 266 | DB00244 O15111 P09917 P05164 P23219 P35354 P0A5L8 P37231 O14920 267 | DB01158 P05023 268 | DB01159 P08100 O60391 P48549 Q9NPC2 P03886 P48051 P59768 Q6W5P4 Q9UN88 P47869 Q16445 P78334 P18507 P98194 P30049 P14867 P28472 O15554 O00591 O14649 Q12791 Q8N1C3 P23415 P18505 O14764 P48169 Q12879 Q8TCU5 P34903 P47870 P31644 Q99928 269 | DB00317 P00533 270 | DB00316 P35354 P23219 271 | DB00726 P28221 P28223 P08913 P46098 P11229 Q01959 P21728 P08588 P14416 P23975 P35368 P08172 P08173 P08909 P28335 P18089 P21918 P31645 P35367 P35348 P13945 P08912 P20309 P08908 P07550 272 | DB00416 Q15822 273 | DB00414 P48048 274 | DB00412 O60488 P37231 275 | DB00413 P28335 P35462 P18825 P28221 P28222 P28223 P18089 P21918 P21728 P08913 P41595 P14416 P08908 P21917 276 | DB00411 P08172 P11229 Q15822 277 | DB00795 P04054 O15111 P09917 P24752 Q9UPY5 P23219 P35354 P37231 O14920 P24557 278 | DB00794 Q9UN88 Q16445 P18507 P18505 O00591 Q99928 O14764 P48169 P47870 Q8N1C3 P31644 P14867 Q13002 P36544 P28472 P47869 P34903 P43681 P42262 P78334 279 | DB00796 P30556 280 | DB00790 P12821 281 | DB00559 P24530 P25101 282 | DB00558 Q9Y3R4 P06818 P27907 283 | DB00555 Q99250 284 | DB00554 P35354 P23219 285 | DB00553 286 | DB00552 P00813 287 | DB00550 P07202 288 | DB00953 P30939 P28221 P28222 289 | DB00952 P30939 P28221 P28222 P08908 290 | DB00774 P00918 P22748 P05023 Q13621 P00915 Q16790 O43570 Q12791 291 | DB00773 P11388 Q02880 292 | DB04573 Q92731 P03372 293 | DB00679 P28223 Q12809 P21728 P14416 P35368 P35348 294 | DB00892 Q9Y5Y9 295 | DB00677 P22303 P06276 296 | DB00674 Q15825 P32297 P22303 Q9GZZ6 P06276 P02708 P30926 Q9UGM1 P11230 P30532 Q05901 P07510 P36544 Q15822 Q04844 P17787 P43681 Q07001 297 | DB00897 Q9UN88 P24046 Q16445 P18507 P18505 A8MPY1 O00591 O14764 P48169 P47870 P28476 Q8N1C3 P31644 P14867 P28472 P30536 P47869 P34903 Q99928 P78334 298 | DB00670 P11229 299 | DB00382 P22303 P06276 300 | DB00381 Q8IZS8 Q6TME4 P17405 P54289 P00915 Q08289 Q00975 Q13936 Q13698 Q01668 301 | DB00703 P00918 P22748 P00915 P43166 302 | DB00700 P08235 303 | DB00253 P04150 304 | DB00706 P35348 P25100 P35368 305 | DB00255 Q92731 P62508 P03372 306 | DB00704 P41145 P35372 Q5T1J1 P41143 307 | DB00708 P41145 P35372 P41143 308 | DB00984 P10275 309 | DB00181 O75899 Q9UBS5 310 | DB00763 P07202 311 | DB00986 P08172 P11229 P20309 312 | DB01203 P07550 P08588 313 | DB00379 P35869 Q14524 314 | DB00981 P22303 315 | DB04878 O43451 316 | DB00175 P04035 317 | DB00177 P30556 318 | DB00170 P00734 P38435 P22891 P16083 P00742 P04070 P08709 P02818 P00740 P07225 Q9BQB6 Q8N0U8 P15559 319 | DB00172 P23284 O43447 P62937 Q53H96 Q7L3T8 Q32P28 O43272 O15460 Q4W8W1 P30405 Q9UN76 P13674 P07814 O94903 P45877 Q8IVL5 Q8IVL6 Q13427 Q99884 P32322 Q96C36 Q96EM0 320 | DB00178 P12821 321 | DB01050 P37231 P07204 P12104 P10415 P35354 P13569 P00750 P23219 322 | DB00989 P22303 P06276 323 | DB01054 O95180 P54289 Q06432 Q08289 Q13936 Q13698 Q01668 Q9NY47 324 | DB00876 P30556 325 | DB00877 P09038 P62942 P42345 326 | DB00870 P35354 P23219 327 | DB00871 P07550 328 | DB00872 P37288 P30518 329 | DB00873 P04150 330 | DB01110 P10613 Q12809 Q9Y691 Q9NS40 O15554 Q16558 Q92952 Q9NPA1 Q9H2S1 P29474 Q86W47 Q9UGI6 Q9H252 P35228 Q12791 331 | DB00506 P10275 P06401 P18405 P03372 332 | DB01120 P15692 Q09428 333 | DB01118 P08588 Q12809 O95180 Q9NY47 334 | DB00589 P28335 P14416 P18825 P28221 P28222 P28223 P41595 P21918 P21728 P08913 P18089 P35462 P08908 P21917 335 | DB00628 Q9UN88 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 Q8N1C3 P31644 P14867 P28472 P30536 P47869 P34903 Q99928 P78334 336 | DB01057 P06276 337 | DB01056 Q14524 338 | DB00580 P35354 339 | DB01114 P35367 P31645 P23975 Q01959 340 | DB00583 P43155 O76082 P05164 Q9H015 O43772 P23141 P50416 P23786 Q8N8R3 Q9UKG9 P47989 341 | DB00584 P12821 342 | DB01115 O95180 Q09470 P54289 P62158 Q08289 Q13936 Q13698 Q01668 343 | DB00586 P09917 P78348 P14555 P35354 P35499 O43525 P23219 O43526 344 | DB00585 P25021 345 | DB00342 P08172 P08173 P11229 Q12809 P08912 P20309 P35367 346 | DB03796 Q14541 P00709 O76054 P08100 P10632 O43617 P09466 P50897 P02689 P83812 Q9X1H9 347 | DB02701 P11439 Q10588 P09874 Q9NXA8 P00338 348 | DB03793 P52758 P14920 Q01234 P0ACQ4 P31992 P51151 O31168 P06993 P30044 P32890 Q5PXQ6 P96965 Q52546 O67987 Q9L9D7 349 | DB00513 P00747 P08519 P00750 350 | DB01182 Q14524 Q12809 351 | DB01180 P12821 352 | DB00517 P08172 P11229 P20309 353 | DB01185 P04150 P10275 P16471 P03372 354 | DB01184 P35462 P14416 355 | DB00519 P12821 356 | DB01188 P05023 357 | DB00457 Q9NS40 Q12809 P25100 P08913 P18089 P35368 Q9H252 P35348 358 | DB00455 P35367 359 | DB02383 O60218 P14550 P15121 360 | DB00780 P21397 P24298 Q9UGI5 P27338 P80404 Q8TD30 Q16853 361 | DB00228 P98194 Q09470 P47869 Q16445 P78334 P18507 P18505 Q8N1C3 P14867 P28472 O15554 O00591 Q9UN88 P48167 P23415 P30049 O14764 P48169 P34903 P47870 P31644 Q99928 P42261 362 | DB01601 Q72874 363 | DB00221 P07550 P08588 364 | DB00731 Q09428 P37231 365 | DB00222 Q09428 Q14654 P48048 366 | DB00736 P20648 367 | DB00227 P20701 Q92769 P04035 368 | DB00734 P28335 P14416 P18825 P28221 P28223 P18089 P21728 P08913 P35462 P35368 P35367 P35348 P21917 P08908 369 | DB01597 P16444 370 | DB01591 P08172 P08173 P08912 P11229 P20309 371 | DB00216 P28566 P28221 P28222 P34969 P41595 P30939 P08908 372 | DB00429 P34995 373 | DB00427 P35367 374 | DB00426 P04293 P09252 375 | DB00357 P05108 P11511 376 | DB00356 Q12791 377 | DB00422 P31645 P23975 Q01959 378 | DB00421 Q6TME4 P31213 P04278 Q06432 P10275 O60840 P04150 P06401 O95180 Q08289 Q9P0X4 O43497 O00555 Q01668 P08235 P54284 Q9H8P0 P18405 Q02641 O00305 P19099 Q9NY47 Q8IZS8 P54289 Q00975 Q13936 Q13698 379 | DB01546 380 | DB00645 Q9Y5Y9 381 | DB00642 P00374 P04818 P22102 P31939 382 | DB00641 P04035 P05107 383 | DB00640 P33765 P29274 P29275 P30542 384 | DB00126 O14832 Q9GZT9 P19021 P09848 Q32P28 P09172 O00469 Q8N543 Q9UHI7 Q96KS0 Q9NVH6 P13674 Q02809 Q9BY66 Q8IVL6 Q54873 Q9NXG6 Q8IVL5 O60568 P24300 Q9H6Z9 O75936 Q6NS38 Q6N063 Q96Q83 385 | DB00123 Q15046 P30825 Q8WY07 O43246 P52569 386 | DB00120 Q9UHI5 P00439 P17735 P07101 Q9Y285 Q9NSD9 O95363 387 | DB00121 P11498 P05166 O00763 P05165 Q13085 P50747 Q96RQ3 Q9HCC0 Q9Y289 388 | DB00908 Q9Y257 O00180 Q14524 Q12809 389 | DB00909 P21397 P35219 P43166 P27338 O60939 P23280 Q9NY72 Q16790 Q99250 P35218 Q8N1Q1 Q14524 O95180 Q9Y2D0 Q9P0X4 O43497 Q8IWT1 Q9NS85 P35499 P35498 O43570 O75493 Q9NY46 P00918 P22748 P07451 Q07699 Q15858 Q9ULX7 P00915 Q9UI33 390 | DB00904 P08908 P35372 Q13639 P28222 P46098 391 | DB00907 Q9Y5Y9 P08172 Q14524 P11229 Q01959 Q9UI33 P31645 P23975 392 | DB00901 P07550 393 | DB00902 P35367 394 | DB00514 P13498 Q15822 P35372 P17787 Q5T1J1 P32297 P04839 P14598 Q99720 P23975 Q15080 P41145 P41143 P19878 O00264 P15153 P43681 P63000 Q8TCU5 P36544 P31645 P30926 395 | DB00633 P08913 396 | DB00461 P35354 P23219 397 | DB00636 Q07869 398 | DB00637 P35367 Q12809 399 | DB01189 P98194 Q09470 P47869 Q16445 P78334 P18507 P18505 P30049 P14867 P28472 O00591 Q8N1C3 Q9UN88 P23415 O14764 P48169 P34903 P47870 P31644 P03886 Q99928 P42261 400 | DB00839 P48048 401 | DB01239 P08172 P08173 P28223 P14416 P20309 P28335 P08912 P41595 P21728 P35462 P35367 P11229 402 | DB01238 P18825 P28221 P28222 P28223 P35462 P46098 P28566 P11229 P34969 P08912 P21728 P08913 P14416 P08172 P08173 P28335 P18089 P21918 P35368 P35367 P35348 P21917 P50406 P20309 P08908 403 | DB01235 P21917 P21918 P21728 P35462 P14416 404 | DB00831 P62158 P63316 P14416 P26447 Q9NYX4 P35348 405 | DB01236 P98194 Q09470 P47869 Q16445 P78334 P18507 P18505 Q8N1C3 P14867 P28472 O00591 Q9UN88 P23415 P30049 O14764 P48169 P34903 P47870 P31644 P03886 Q99928 P42261 406 | DB00834 P04150 P06401 407 | DB01233 P46098 P11229 P14416 Q13639 408 | DB01119 P00918 P05023 Q14654 P00915 P55017 Q12791 409 | DB01427 Q07343 Q14432 P01375 410 | DB01303 Q92769 Q14432 P29274 P27815 P30542 411 | DB01161 Q8TCU5 Q9GZZ6 Q9Y5Y9 Q01959 P46098 412 | DB01160 P43119 P43088 413 | DB00462 P08172 P11229 P20309 414 | DB00788 P35354 P23219 415 | DB00467 P11388 P43700 P43702 416 | DB00465 P35354 P23219 417 | DB00783 Q92731 O75469 P03372 418 | DB00469 P35354 P23219 419 | DB00468 P14770 O15554 420 | DB00787 P04293 P03176 P09252 421 | DB00784 P35354 P23219 422 | DB01016 Q09428 Q14654 P48048 O95342 Q15842 O60706 P50416 P13569 P48544 O95477 423 | DB01011 P00183 P15538 424 | DB00549 Q9Y271 425 | DB00205 P00374 P13922 426 | DB00544 P04818 427 | DB00545 P22303 P06276 428 | DB00540 P18825 P28223 P08913 P11229 Q99720 P08912 P08588 P14416 P23975 P35368 P08172 P08173 P08909 P28335 P18089 P31645 P35367 O00264 P35348 P13945 P25100 P50406 P20309 P08908 P07550 429 | DB01018 P08913 P18089 430 | DB00542 P12821 431 | DB00543 P18825 P28222 P28223 Q01959 P35462 P46098 P11229 Q9H3N8 P34969 P21728 P08912 P08913 P14416 P47869 P34903 P23975 P78334 P08172 P08173 P18507 P18505 P28335 P41595 Q8N1C3 P14867 P28472 P18089 P35368 P35367 O00591 P35348 P21917 Q9UN88 Q16445 O14764 P48169 P25100 P50406 P47870 P20309 P08908 P31644 P31645 Q99928 432 | DB00764 P04150 433 | DB00765 P07101 434 | DB00944 P22303 P06276 435 | DB00945 Q9Y478 Q04828 O14920 P25963 O43741 P19838 Q9UGI9 P25101 P51812 P35354 P54646 Q00653 P04637 P11021 P54619 Q13131 P23219 Q9UGJ0 436 | DB00762 Q969P6 P11387 437 | DB00947 P03372 438 | DB00603 P06401 P03372 439 | DB00949 Q8TCU5 Q13224 Q12879 440 | DB00601 441 | DB00606 P00918 P22748 P00915 P54710 442 | DB00605 P35354 P27361 P15121 Q03181 Q9Y5Y4 P23219 443 | DB00604 P28223 P46098 Q12809 Q13639 444 | DB00863 P25021 445 | DB00226 P23975 446 | DB00304 P06401 P03372 447 | DB00305 448 | DB00302 P00747 449 | DB00264 P07550 P08588 450 | DB00266 Q08257 P15559 Q9BQB6 451 | DB00308 Q9NS40 Q06432 O00180 Q12809 P54289 Q14654 Q9Y257 Q13936 Q02641 Q9H252 452 | DB00117 P19113 Q99624 P12081 P42357 453 | DB00287 P43088 454 | DB00890 P03372 455 | DB00284 P14410 P10253 O43451 P04746 456 | DB00283 P35367 457 | DB00281 Q15858 Q14524 Q9Y5Y9 P00533 458 | DB00280 P08172 Q14524 P11229 Q9NZV8 P20309 Q9UK17 459 | DB00288 P04150 P04083 460 | DB00161 P26640 P05166 P54687 461 | DB00166 Q9Y234 Q9Y289 O43766 462 | DB00167 P41252 P45954 O15382 Q9NSE4 P54687 463 | DB00169 P11473 464 | DB00728 P08172 P46098 Q15822 465 | DB00312 O60391 O15399 P47869 Q16445 P78334 Q05586 Q13002 P18507 P18505 Q8N1C3 P14867 Q14957 O00591 Q9UN88 P28472 P43681 O14764 P48169 Q13224 Q12879 Q8TCU5 P34903 P47870 P36544 P31644 Q99928 P42262 466 | DB00315 P30939 P28221 P28222 P08908 467 | DB02021 P15121 468 | DB02020 P15121 469 | DB03419 P0AA43 Q12882 Q8X444 P77836 P10186 Q7WYV4 Q9NWZ5 470 | DB00841 P07550 P08588 471 | DB00843 P22303 P28223 472 | DB00842 Q9UN88 P24046 Q16445 P18507 P18505 A8MPY1 O00591 O14764 P48169 P47870 P28476 Q8N1C3 P31644 P14867 P28472 P47869 P34903 Q99928 P78334 473 | DB00846 P04150 474 | DB00859 475 | DB03395 P00797 476 | DB01340 P12821 477 | DB01907 P09601 P40939 P07864 Q16878 P03891 P00374 O14561 P13995 P40394 Q00796 P51553 P56937 Q9UDR5 P00390 P00326 P20839 P40925 P49821 P40926 O43920 P30043 Q9UBM7 Q02338 Q08426 O95479 P48728 P00367 O14556 Q13630 P31937 Q6ZMR3 P30519 P03897 P00387 O00483 P53004 P29803 P52895 Q9Y6M9 P47895 P15121 Q16795 O96000 P08559 Q16798 P16219 P08319 P30838 P03886 P26439 P43353 P49189 P30837 P19404 Q16718 Q02252 P03905 P80365 P03901 P03923 O75438 P51970 P42330 Q92781 Q99714 O95139 P07327 Q04828 P28845 P05093 P05091 O43181 O43674 P21695 O43837 P56556 P03915 P12268 O43175 O75380 Q9NRX3 Q16836 O75306 Q15738 P04035 P17568 P48448 O00217 P49448 O95182 P04406 P10515 P17516 P00338 P09417 O43677 P11177 O43678 O15239 O75251 Q86Y39 O43676 P11766 Q15800 P50213 P00325 P48163 P32322 P15428 Q96C36 O95298 O95299 Q9P0J0 P07195 P14679 Q9BYZ2 Q13423 P51648 P51649 O60701 P56181 P30038 O75489 P09622 P00352 O94788 O95168 O95169 P23368 O95167 Q02928 P11586 P51659 Q02218 Q92506 P28331 O95178 P16083 Q9UI09 P14061 P14060 P49419 P37059 P37058 478 | DB01046 P51788 479 | DB01047 P04150 Q99835 480 | DB00894 P11511 481 | DB01045 P0A8V2 O75469 P0A8T7 482 | DB01042 483 | DB01067 Q09428 P37231 484 | DB00999 P00918 P22748 P00915 P55017 Q16790 O43570 Q12791 485 | DB00657 Q15822 486 | DB00500 P35354 P23219 487 | DB00501 P25021 488 | DB00502 P21728 P28223 Q13224 P14416 P35462 489 | DB01193 P07550 P08588 490 | DB01194 P00918 P22748 P00915 P35218 491 | DB01195 Q14524 P35499 492 | DB01196 Q92731 P11137 P78559 P03372 493 | DB01197 P14780 P08253 P09960 P12821 494 | DB01199 P22303 P46098 Q15822 495 | DB00591 P04150 496 | DB00481 Q92731 P03372 497 | DB00482 P35354 O15530 498 | DB00484 P18825 P08913 P18089 499 | DB00338 P20648 500 | DB00721 P21397 Q16558 O15554 Q15822 Q9Y691 P27338 Q01959 Q8TCU5 Q92952 Q9Y5Y9 Q86W47 P46098 Q9NPA1 Q9UGI6 Q9H2S1 Q12791 501 | DB00335 P07550 P08588 502 | DB00723 P35348 P25100 P35368 503 | DB00725 P08172 P08173 P08912 P11229 P20309 504 | DB00331 Q9Y478 505 | DB00727 P16066 506 | DB00799 P28702 P10276 P10826 P13631 507 | DB00594 P19801 P19634 P78348 Q16515 P37088 P51170 P51172 P51168 P00749 508 | DB00350 P23219 P48048 509 | DB01124 Q09428 P48048 510 | DB00349 Q9UN88 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 Q8N1C3 P31644 P14867 P28472 P47869 P34903 Q99928 P78334 511 | DB01121 P35498 512 | DB00439 P04035 513 | DB01122 P22303 514 | DB00434 P08172 P28223 P11229 P28335 P20309 P35367 515 | DB00343 Q06432 516 | DB00340 P08172 P08173 P08912 P11229 P20309 517 | DB00437 P47989 518 | DB00346 Q12809 P35348 P25100 P35368 519 | DB01128 P10275 520 | DB00432 P04818 521 | DB00433 P14416 522 | DB00654 P43088 523 | DB00655 P03372 524 | DB00158 P41439 P14207 525 | DB00650 P04818 526 | DB00651 Q13946 P27815 Q9NP56 Q08493 Q07343 Q08499 P29274 P30542 527 | DB00652 P41145 P35372 Q99720 528 | DB00653 Q06432 P54289 Q08289 Q13936 Q02641 Q13698 529 | DB00153 P11473 530 | DB00334 P18825 P28221 P28222 P28223 P35462 P46098 P47898 P48169 P28566 P11229 Q9H3N8 P34969 P08912 P21728 P08913 P13945 P14416 P47869 P34903 P78334 P08172 P08173 P18507 P18505 P28335 P18089 Q8N1C3 P21918 P14867 P28472 P41595 P35368 P35367 O00591 P35348 P21917 Q9UN88 Q16445 P08588 O14764 P25021 P50406 P47870 P20309 P08908 P31644 P07550 Q99928 531 | DB00962 P14867 P30536 532 | DB00156 Q9BW92 P26639 Q8IYQ7 533 | DB00939 O43525 P35354 P09917 P23219 O43526 534 | DB00938 P07550 535 | DB00933 P28223 P14416 536 | DB00935 P18825 P25100 P08913 P18089 P35368 P35348 537 | DB00934 P08172 P08173 P28335 P11229 P18825 P28223 P25100 P18089 P08912 P14416 P34969 P20309 P35368 P35367 P23975 P35348 P08913 538 | DB00936 P35354 Q04828 P23219 539 | DB00990 P11511 540 | DB00237 Q9UN88 Q16445 P18507 P18505 O00591 O14764 P48169 P47870 P43681 Q8N1C3 P31644 P14867 P28472 P36544 P47869 Q13002 P34903 Q99928 P42262 P78334 541 | DB00235 Q14432 542 | DB00232 P00918 P22748 Q13621 P00915 543 | DB00436 P55017 P00918 P22748 P00915 Q12791 544 | DB01157 P00374 545 | DB00239 P10613 Q04782 546 | DB00744 P09917 547 | DB00806 P27815 P21589 O76074 Q07343 P29274 P30542 548 | DB00801 Q9UN88 P24046 P28472 P18507 P18505 A8MPY1 O00591 O14764 P48169 P47870 P28476 Q8N1C3 P31644 P14867 Q16445 P47869 P34903 Q99928 P78334 549 | DB00800 P18825 P18089 P21918 P21728 P08913 P25100 P35368 P35348 550 | DB00562 P00918 P22748 P00915 P55017 Q16790 O43570 551 | DB00809 P08172 P08173 P11229 P20309 552 | DB00808 P51787 P15382 553 | DB01228 Q14524 554 | DB04818 555 | DB01222 P04150 556 | DB01223 Q92769 Q14432 P33765 P30542 557 | DB01221 P08172 P41145 P28335 P11229 P08173 P41143 P20309 P28222 P28223 P28221 P25103 P41595 Q8TCU5 P35372 P14416 P08912 P28566 P30939 P23975 P08908 558 | DB01227 P32297 P35372 P30926 559 | -------------------------------------------------------------------------------- /database/kegg.txt: -------------------------------------------------------------------------------- 1 | D06172 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 2 | D03415 hsa154 hsa155 hsa147 hsa146 hsa153 hsa148 3 | D04006 hsa136 hsa134 hsa135 4 | D00216 hsa2548 hsa8972 hsa2595 hsa279 hsa280 5 | D00198 6 | D00462 hsa367 7 | D00211 8 | D00195 hsa4988 9 | D00196 hsa43 10 | D00219 hsa6833 11 | D00218 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 12 | D02150 hsa154 hsa155 hsa153 13 | D02098 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 14 | D00609 hsa148 hsa147 hsa146 15 | D00358 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 16 | D00359 hsa3156 17 | D00356 hsa5737 18 | D02092 hsa3360 19 | D00606 hsa150 hsa151 hsa152 20 | D00607 hsa150 hsa146 hsa152 hsa148 hsa147 hsa151 21 | D00601 hsa153 22 | D00351 hsa1586 23 | D01180 hsa3283 24 | D01228 hsa43 25 | D00274 hsa3360 26 | D01183 hsa5743 hsa5742 27 | D03252 hsa6714 hsa25 28 | D02655 29 | D01223 hsa7298 30 | D01227 hsa1910 hsa1909 31 | D00994 hsa43 32 | D00995 hsa43 33 | D00996 hsa154 hsa155 hsa150 hsa146 hsa152 hsa153 hsa148 hsa147 hsa151 34 | D02076 hsa150 hsa151 hsa152 35 | D02070 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 36 | D01918 hsa1991 37 | D00586 hsa367 38 | D00585 hsa5241 39 | D00584 hsa7298 40 | D00998 hsa43 41 | D02374 hsa154 hsa155 hsa153 42 | D02375 43 | D01441 hsa3815 hsa5159 hsa5156 hsa25 44 | D02566 hsa6530 45 | D00652 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 46 | D00451 hsa3352 hsa3351 47 | D00217 48 | D00452 49 | D00107 hsa11261 hsa5530 hsa5532 hsa5533 hsa5534 hsa5535 hsa63928 50 | D00106 hsa5739 51 | D00105 hsa2100 hsa2099 52 | D00456 hsa170572 hsa285242 hsa9177 hsa200909 hsa3359 53 | D00459 hsa1636 54 | D00109 hsa5743 hsa5742 55 | D00059 hsa1816 hsa1815 hsa1814 hsa1813 hsa1812 56 | D00518 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 57 | D00519 hsa6559 58 | D00691 59 | D00690 hsa2908 60 | D00697 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 61 | D01350 62 | D01825 hsa2908 63 | D01352 hsa5737 64 | D02578 hsa3358 65 | D01981 hsa2147 66 | D00512 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 67 | D00513 hsa154 hsa155 hsa153 68 | D00514 hsa150 hsa151 hsa152 69 | D01358 hsa150 hsa151 hsa152 hsa148 hsa3356 hsa3357 hsa3354 hsa3355 hsa3352 hsa3350 hsa3351 hsa147 hsa3358 hsa146 70 | D01454 hsa154 hsa155 hsa153 71 | D00640 hsa153 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 72 | D02207 hsa1145 hsa1140 hsa1134 hsa1144 hsa1146 73 | D02204 hsa1138 hsa1139 hsa1143 hsa1142 hsa1141 hsa1135 hsa1136 hsa1137 hsa55584 hsa8973 hsa57053 74 | D00487 hsa43 75 | D00222 76 | D02176 77 | D05156 78 | D00658 hsa6559 79 | D00483 hsa154 hsa155 hsa153 80 | D00775 81 | D00771 82 | D00779 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 83 | D01994 hsa3360 84 | D02272 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 85 | D00079 hsa5732 86 | D00075 hsa367 87 | D02278 88 | D00613 hsa1812 89 | D00070 90 | D00960 hsa1588 91 | D03180 92 | D03187 hsa5732 93 | D01704 hsa5144 hsa5141 hsa5143 hsa5142 94 | D00410 hsa1584 95 | D01071 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 96 | D03670 97 | D02173 hsa43 98 | D00604 hsa150 hsa151 hsa152 99 | D03077 hsa1636 100 | D00354 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 101 | D00646 hsa1131 102 | D03758 hsa1636 103 | D03756 hsa1636 104 | D03753 hsa1636 105 | D03752 hsa1636 106 | D03751 hsa43 107 | D00312 hsa2100 hsa2099 108 | D02580 hsa4128 hsa4129 109 | D04048 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 110 | D01386 hsa154 hsa155 hsa150 hsa146 hsa152 hsa153 hsa148 hsa147 hsa151 111 | D01387 hsa2908 112 | D00315 hsa5743 113 | D00252 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 114 | D00251 hsa1636 115 | D00319 hsa778 hsa779 hsa775 hsa776 116 | D00255 hsa154 hsa155 hsa147 hsa146 hsa153 hsa148 117 | D03829 118 | D03828 hsa7298 hsa2618 119 | D03350 hsa1956 hsa2066 hsa2065 hsa2064 120 | D02614 hsa153 121 | D01964 hsa5737 122 | D05938 hsa4988 123 | D03823 124 | D03822 hsa43 hsa590 125 | D03826 hsa43 126 | D03218 hsa2321 hsa5159 hsa2324 hsa3815 hsa3791 127 | D01578 hsa5743 hsa5742 128 | D01575 129 | D03210 hsa1234 130 | D03217 hsa8654 131 | D02756 hsa7155 hsa7150 hsa7153 132 | D00950 hsa5241 133 | D00951 hsa5241 134 | D01811 hsa5743 hsa5742 135 | D00954 hsa5241 136 | D00956 hsa367 137 | D02328 hsa231 138 | D00760 hsa1145 hsa1140 hsa1134 hsa1144 hsa1146 139 | D04375 hsa150 hsa151 hsa152 140 | D01603 141 | D00656 hsa6559 142 | D00655 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 143 | D00654 hsa6559 144 | D00653 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 145 | D00559 hsa1815 hsa1814 hsa1813 146 | D00651 hsa6559 147 | D00650 hsa6559 148 | D00554 hsa2100 hsa2099 149 | D00227 hsa50940 hsa8622 hsa5151 hsa27115 hsa5144 hsa5150 hsa10846 hsa5141 hsa5140 hsa5143 hsa5142 hsa134 hsa135 hsa5138 hsa5139 hsa5153 hsa5136 hsa5137 150 | D00224 hsa7498 151 | D00225 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 152 | D00550 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 153 | D00552 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 154 | D00553 hsa8911 hsa6336 hsa6326 hsa6332 hsa6331 hsa6323 hsa6328 155 | D00687 hsa154 156 | D01253 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 157 | D03450 158 | D01256 159 | D01709 hsa5743 hsa5742 160 | D00380 hsa6833 hsa3767 161 | D00383 hsa1636 162 | D02101 hsa4988 163 | D00387 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 164 | D05113 hsa4988 hsa4986 hsa4985 165 | D02327 hsa3269 166 | D01925 hsa7201 167 | D02321 hsa7155 hsa7153 168 | D02323 hsa231 169 | D01854 hsa6833 170 | D00032 171 | D00037 172 | D00036 173 | D00035 174 | D00039 175 | D00038 176 | D00810 hsa5743 hsa5742 177 | D00813 hsa5743 hsa5742 178 | D00139 179 | D00232 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 180 | D00816 hsa6530 hsa6532 181 | D00132 hsa5743 hsa5742 182 | D00421 hsa1636 183 | D00130 hsa5743 hsa5742 184 | D00131 hsa219 hsa217 hsa224 185 | D00136 hsa1813 186 | D00425 hsa5743 187 | D00426 hsa3356 hsa147 hsa148 hsa1813 hsa146 188 | D00845 hsa4988 189 | D00369 190 | D01911 hsa7155 hsa7150 hsa7153 191 | D03037 hsa3757 hsa3784 192 | D03034 193 | D00168 194 | D00045 hsa136 hsa134 hsa135 hsa140 195 | D00043 hsa43 hsa590 196 | D00040 197 | D00041 198 | D00726 hsa1813 hsa170572 hsa3360 hsa285242 hsa200909 hsa9177 hsa3359 199 | D00049 200 | D01782 hsa3269 201 | D01108 202 | D03788 hsa1991 203 | D03621 hsa3269 204 | D03784 hsa1588 205 | D01103 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 206 | D03781 hsa1588 207 | D00812 hsa6530 hsa6532 208 | D00964 hsa1588 209 | D01690 hsa5139 hsa5140 210 | D00103 211 | D01692 hsa148 hsa147 hsa146 212 | D03765 hsa1636 213 | D03763 hsa1636 214 | D03760 hsa1636 215 | D01699 hsa1131 216 | D01862 hsa3156 217 | D00188 hsa7421 218 | D02409 219 | D00183 220 | D00182 hsa5241 221 | D00180 hsa5731 222 | D00187 hsa7421 223 | D00422 hsa3274 224 | D00185 hsa2100 hsa2099 225 | D00454 hsa1815 hsa1814 hsa147 hsa146 hsa148 hsa3356 hsa1813 hsa3269 hsa3358 226 | D00619 hsa3763 hsa775 hsa776 hsa778 hsa779 hsa3747 hsa3743 hsa3738 hsa3744 227 | D01217 hsa5241 228 | D00349 hsa778 hsa779 hsa775 hsa776 229 | D00348 hsa5916 hsa5914 hsa5915 230 | D02147 hsa154 231 | D01745 hsa1813 232 | D00345 hsa6559 233 | D00611 hsa1138 hsa1139 hsa1143 hsa1142 hsa1145 hsa1144 hsa1140 hsa1141 hsa1146 hsa1134 hsa1135 hsa1136 hsa1137 hsa55584 hsa8973 hsa57053 234 | D00688 hsa154 235 | D00617 hsa778 hsa779 hsa775 hsa776 236 | D00340 hsa6559 237 | D00615 hsa778 hsa779 hsa775 hsa776 238 | D00342 239 | D03712 hsa5743 hsa5742 240 | D03710 hsa5743 241 | D03717 hsa5743 242 | D03716 hsa5743 243 | D01582 hsa5743 hsa5742 244 | D01198 hsa5139 hsa5140 245 | D03490 hsa154 hsa155 hsa153 246 | D01196 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 247 | D03365 248 | D02368 hsa6240 249 | D02042 hsa5139 hsa5140 250 | D01969 hsa778 hsa779 hsa775 hsa776 251 | D00987 hsa1813 252 | D01840 hsa6093 hsa9475 253 | D01842 hsa231 254 | D02361 255 | D02360 hsa6532 256 | D02362 hsa6532 257 | D01966 hsa29881 258 | D05407 259 | D01965 hsa148 hsa147 hsa146 260 | D05792 hsa154 261 | D00506 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 262 | D00505 hsa4128 hsa4129 263 | D00504 264 | D00503 hsa1813 265 | D02441 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 266 | D00501 267 | D00464 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 268 | D00465 hsa1131 hsa1129 hsa1128 269 | D00509 hsa150 hsa146 hsa152 hsa148 hsa147 hsa151 270 | D00463 hsa5743 hsa5742 271 | D01448 hsa1813 272 | D01364 273 | D01367 hsa2908 274 | D00666 hsa3269 275 | D00667 hsa43 276 | D02564 hsa4128 hsa4129 277 | D02563 hsa4128 278 | D02562 hsa4129 279 | D02561 hsa4128 280 | D02560 hsa4128 281 | D02214 hsa7155 hsa7153 282 | D02217 hsa2100 hsa2099 283 | D05246 hsa185 284 | D00480 hsa3269 285 | D01462 hsa1813 hsa3350 286 | D00694 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 287 | D00488 288 | D00510 hsa5743 hsa5742 289 | D05129 290 | D01453 hsa50940 hsa8622 hsa5151 hsa27115 hsa5144 hsa5150 hsa10846 hsa5141 hsa5140 hsa5143 hsa5142 hsa136 hsa134 hsa135 hsa5138 hsa5139 hsa5153 hsa5136 hsa5137 291 | D01450 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 292 | D00762 hsa7054 293 | D00761 294 | D01828 295 | D00765 hsa1145 hsa1140 hsa1134 hsa1144 hsa1146 296 | D01984 hsa495 hsa496 297 | D00769 hsa64805 298 | D00515 299 | D00160 hsa5340 300 | D00163 hsa9971 301 | D02289 hsa2908 302 | D00002 303 | D00169 hsa5319 hsa8399 hsa50487 hsa84647 hsa391013 hsa5743 hsa5742 hsa26279 hsa30814 hsa5320 hsa81579 hsa5322 hsa64600 304 | D00007 305 | D00005 306 | D00548 307 | D01064 hsa7298 308 | D02826 hsa3352 hsa3351 309 | D01061 hsa7150 310 | D02698 hsa7155 hsa7153 311 | D01069 hsa1636 312 | D01689 hsa2908 313 | D00649 hsa6337 hsa6340 hsa6338 314 | D01688 hsa231 315 | D00715 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 316 | D00711 hsa2902 hsa2903 hsa2904 hsa2905 hsa2906 317 | D00631 hsa3762 hsa3760 hsa3767 hsa775 hsa776 hsa3764 hsa778 hsa779 hsa3768 hsa3741 hsa3757 hsa8911 hsa3752 hsa8913 hsa8912 hsa3784 318 | D03728 hsa2147 319 | D01332 hsa3269 320 | D03722 hsa2147 321 | D03858 322 | D03720 hsa2339 hsa2342 323 | D03689 324 | D00301 325 | D00300 hsa3269 326 | D00303 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 327 | D01397 hsa5743 hsa5742 328 | D00306 hsa1268 hsa1269 329 | D02088 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 330 | D00391 331 | D01665 hsa2548 hsa8972 hsa2595 332 | D01667 hsa1636 333 | D00397 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 334 | D01549 hsa1636 335 | D02110 hsa5743 hsa5742 336 | D02579 hsa4128 hsa4129 337 | D01097 hsa4129 338 | D01547 hsa5743 hsa5742 339 | D02087 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 340 | D03433 341 | D00279 hsa5465 342 | D02082 hsa185 343 | D02333 344 | D01324 hsa3269 345 | D00623 hsa1636 346 | D00549 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 347 | D00621 hsa1636 348 | D00627 hsa185 349 | D03966 hsa2912 350 | D00625 hsa2548 hsa8972 hsa2595 351 | D00543 352 | D00542 353 | D00629 hsa778 hsa779 hsa775 hsa776 354 | D00547 355 | D00546 356 | D00545 357 | D00544 358 | D03442 359 | D03440 hsa1636 360 | D01718 hsa5742 361 | D01715 362 | D01717 hsa3269 363 | D01243 hsa8911 hsa6336 hsa6326 hsa6332 hsa6331 hsa6323 hsa6328 364 | D01242 hsa3269 365 | D01713 hsa3269 366 | D01712 hsa50940 hsa8622 hsa5151 hsa27115 hsa5144 hsa5150 hsa10846 hsa5141 hsa5140 hsa5143 hsa5142 hsa134 hsa135 hsa5138 hsa5139 hsa5153 hsa5136 hsa5137 367 | D02258 hsa3156 368 | D02315 369 | D02250 hsa6751 hsa6753 hsa6752 hsa6755 hsa6754 370 | D02253 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 371 | D02262 372 | D02338 hsa153 373 | D00437 hsa778 hsa779 hsa775 hsa776 374 | D00434 hsa3156 375 | D00432 hsa154 hsa155 hsa153 376 | D01390 hsa154 hsa155 hsa153 377 | D04979 378 | D03816 hsa3156 379 | D00438 hsa778 hsa779 hsa775 hsa776 380 | D00129 hsa7421 381 | D00281 hsa150 hsa151 hsa152 382 | D01432 hsa7150 383 | D02007 hsa7201 384 | D00285 385 | D00538 hsa8912 hsa8913 hsa6335 hsa8911 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 386 | D00539 hsa8911 hsa8913 hsa8912 387 | D00536 388 | D00537 hsa2900 hsa2901 hsa775 hsa2898 hsa2899 hsa778 hsa2897 hsa6323 hsa2892 hsa2893 hsa2890 hsa2891 hsa6326 hsa6334 hsa776 hsa779 hsa6331 hsa6328 hsa6329 hsa6335 389 | D00826 hsa4128 hsa4129 390 | D00535 hsa18 391 | D00125 hsa7155 hsa7153 392 | D00533 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 393 | D02558 hsa43 hsa590 394 | D02559 hsa4128 395 | D02066 hsa154 hsa155 hsa153 396 | D04716 hsa4988 397 | D00593 hsa6833 398 | D01022 hsa148 hsa147 hsa146 399 | D01020 hsa148 hsa147 hsa146 400 | D01027 401 | D01024 hsa148 hsa147 hsa146 402 | D02914 hsa778 hsa779 hsa775 hsa776 403 | D02910 hsa3762 hsa3760 hsa3767 hsa3784 hsa3764 hsa3752 hsa3768 hsa3757 hsa5243 hsa148 hsa147 hsa153 hsa146 404 | D01907 405 | D00055 406 | D00054 407 | D00759 hsa1145 hsa1140 hsa1134 hsa1144 hsa1146 408 | D00050 409 | D00052 410 | D00752 hsa3615 hsa3614 411 | D00753 hsa2475 412 | D04370 hsa170572 hsa285242 hsa9177 hsa200909 hsa3359 413 | D03991 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 414 | D00455 hsa495 hsa496 415 | D04625 hsa154 416 | D04092 hsa4128 hsa4129 417 | D01425 418 | D01115 hsa4306 419 | D05461 420 | D01111 hsa6833 421 | D01294 hsa5241 422 | D01295 hsa3269 423 | D01297 hsa1128 424 | D02279 425 | D01119 hsa1636 426 | D01118 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 427 | D03787 hsa1621 428 | D02709 hsa5743 429 | D03798 hsa79001 430 | D03654 hsa1131 431 | D01051 hsa3356 hsa3363 hsa1813 hsa3350 432 | D03775 hsa1636 433 | D03776 hsa1636 434 | D03773 hsa1636 435 | D03772 hsa1636 436 | D03778 hsa1588 437 | D00780 hsa1813 438 | D00270 hsa1129 hsa1128 hsa150 hsa151 hsa152 hsa1131 hsa1132 hsa1133 hsa3356 hsa3357 hsa1813 hsa3269 hsa3358 439 | D04023 hsa1956 440 | D04024 hsa1956 hsa2064 441 | D04025 hsa2064 442 | D04028 hsa5319 hsa5406 hsa5407 hsa1056 hsa50487 hsa84647 hsa5408 hsa391013 hsa8399 hsa26279 hsa30814 hsa5320 hsa81579 hsa5322 hsa64600 443 | D04029 hsa2159 444 | D03503 hsa3274 445 | D00379 hsa6833 446 | D02671 447 | D03208 hsa5972 448 | D00371 hsa50940 hsa8622 hsa5151 hsa27115 hsa5144 hsa5150 hsa10846 hsa5141 hsa5140 hsa5143 hsa5142 hsa134 hsa135 hsa5138 hsa5139 hsa5153 hsa5136 hsa5137 449 | D03845 hsa771 hsa377677 hsa768 hsa767 hsa766 hsa765 hsa763 hsa762 hsa761 hsa760 hsa23632 hsa759 hsa11238 450 | D00373 hsa1813 451 | D02008 hsa8654 452 | D00377 453 | D03274 hsa1129 hsa1128 hsa150 hsa151 hsa152 hsa1131 hsa1132 hsa1133 hsa3356 hsa3357 hsa1813 hsa3269 hsa3358 454 | D00419 hsa5732 455 | D01599 hsa6833 456 | D01871 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 457 | D02194 hsa1800 458 | D00228 hsa6530 hsa6532 459 | D02193 hsa43 460 | D01973 hsa3352 hsa3351 461 | D00148 462 | D01974 hsa5743 463 | D00970 hsa5743 hsa5742 464 | D00086 465 | D01977 hsa1956 466 | D00088 hsa2908 467 | D00145 468 | D01475 hsa5743 hsa5742 469 | D02451 hsa1588 470 | D00809 hsa6530 hsa6532 471 | D00577 hsa2100 hsa2099 472 | D00574 hsa1588 473 | D00411 hsa10800 474 | D00579 475 | D00477 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 476 | D00475 477 | D00474 478 | D01276 479 | D01275 hsa7155 hsa7153 480 | D06056 hsa3360 481 | D00675 hsa3352 hsa3351 482 | D00674 hsa3354 hsa3355 hsa3352 hsa3350 hsa3351 483 | D00677 hsa170572 hsa285242 hsa9177 hsa200909 hsa3359 484 | D00676 hsa3352 hsa3351 485 | D00670 hsa43 486 | D00673 hsa3274 487 | D00904 hsa5743 hsa5742 488 | D00495 489 | D00494 hsa3269 490 | D00394 hsa6530 hsa6532 491 | D00493 hsa1813 492 | D04999 493 | D00499 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 494 | D00498 hsa4988 hsa4986 495 | D00889 hsa3156 496 | D00882 497 | D00887 hsa3156 498 | D00884 499 | D00885 500 | D00684 hsa154 501 | D00798 hsa1813 502 | D00799 hsa1813 503 | D00790 504 | D00791 hsa1813 505 | D02354 506 | D02355 hsa5743 hsa5742 507 | D02356 hsa3763 hsa775 hsa776 hsa778 hsa779 hsa3747 hsa3743 hsa3738 hsa3744 508 | D02546 hsa2900 hsa2897 hsa2901 hsa2898 hsa2899 509 | D02350 hsa5743 hsa5742 510 | D00155 hsa100 511 | D00158 hsa5743 hsa5742 512 | D02358 hsa153 513 | D02290 hsa5743 hsa5742 514 | D00400 hsa185 515 | D00401 hsa7173 516 | D00299 hsa7421 517 | D00126 hsa5743 hsa5742 518 | D00683 hsa154 519 | D01844 hsa2159 520 | D04197 hsa7298 521 | D00127 hsa5743 hsa5742 522 | D02835 hsa231 523 | D00318 hsa3274 524 | D05341 525 | D00295 hsa3274 526 | D02461 527 | D03115 hsa6093 hsa9475 528 | D00293 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 529 | D00018 530 | D00520 hsa3269 531 | D02349 hsa154 hsa155 hsa150 hsa146 hsa152 hsa153 hsa148 hsa147 hsa151 532 | D00704 533 | D00703 534 | D00709 hsa9900 535 | D00708 536 | D03738 hsa5972 537 | D04066 538 | D00665 hsa3269 539 | D03767 hsa1636 540 | D03731 hsa4129 541 | D03733 hsa4129 542 | D03735 hsa6646 hsa8435 543 | D03734 hsa6646 hsa8435 544 | D03736 hsa6300 hsa5603 hsa5600 hsa1432 545 | D03803 hsa231 546 | D00338 547 | D03806 hsa231 548 | D03805 hsa231 549 | D00335 hsa6833 550 | D00336 hsa6833 hsa3767 551 | D00330 hsa5743 hsa5742 552 | D00331 hsa6557 hsa2557 hsa2556 hsa2555 hsa2554 hsa2559 hsa2558 hsa6558 553 | D00332 hsa781 hsa9254 hsa93589 hsa55799 554 | D00333 555 | D02884 556 | D02731 hsa8654 557 | D02880 558 | D01126 559 | D02630 hsa8911 hsa8913 hsa8912 560 | D01122 hsa5743 hsa5742 561 | D01652 hsa5724 562 | D06238 hsa1719 hsa200895 563 | D00014 564 | D03601 hsa3156 565 | D00678 hsa170572 hsa285242 hsa9177 hsa200909 hsa3359 566 | D00622 hsa1636 567 | D01325 hsa5743 hsa5742 568 | D00620 hsa1636 569 | D00903 hsa5743 hsa5742 570 | D01891 hsa5732 571 | D00902 572 | D03769 hsa1636 573 | D00208 574 | D00630 575 | D00633 hsa1816 hsa1815 hsa1814 hsa1813 hsa1812 576 | D00632 hsa153 577 | D01885 hsa7155 hsa7153 578 | D00624 hsa1636 579 | D00636 hsa3762 hsa3760 hsa3767 hsa3784 hsa3764 hsa3752 hsa3768 hsa3757 hsa5243 hsa148 hsa147 hsa153 hsa146 580 | D00639 hsa6326 hsa6335 hsa6331 hsa6323 hsa6328 hsa6329 581 | D01888 hsa4128 hsa4129 582 | D02086 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 583 | D00203 584 | D00151 hsa5743 hsa5742 585 | D03878 hsa2902 hsa2903 hsa2904 hsa2905 hsa2906 586 | D03879 hsa153 587 | D00235 hsa153 588 | D00234 hsa3269 589 | D01370 590 | D01236 hsa552 hsa554 591 | D00837 hsa4988 hsa4986 592 | D01900 hsa1636 593 | D00540 hsa1131 594 | D00594 hsa6833 595 | D00596 hsa5468 596 | D02308 597 | D00598 hsa153 598 | D00231 hsa5139 hsa5140 599 | D02304 600 | D05024 hsa8911 hsa8913 hsa8912 601 | D02068 hsa43 602 | D02485 603 | D00496 604 | D00733 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 605 | D02487 606 | D00442 hsa6751 hsa6753 hsa6752 hsa6755 hsa6754 607 | D00443 hsa4306 608 | D00440 hsa3274 609 | D00110 hsa6532 hsa3757 hsa6336 hsa6326 hsa6531 hsa6530 hsa6331 hsa6323 hsa6328 hsa6329 hsa6335 610 | D00113 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 611 | D00153 hsa1588 612 | D00448 613 | D00449 614 | D00118 hsa5743 hsa5742 615 | D00186 616 | D01346 617 | D00930 hsa7421 618 | D00838 hsa4988 hsa4986 hsa4985 619 | D02017 hsa50940 hsa8622 hsa5151 hsa27115 hsa5144 hsa5150 hsa10846 hsa5141 hsa5140 hsa5143 hsa5142 hsa134 hsa135 hsa5138 hsa5139 hsa5153 hsa5136 hsa5137 620 | D00528 hsa50940 hsa8622 hsa5151 hsa27115 hsa5144 hsa5150 hsa10846 hsa5141 hsa5140 hsa5143 hsa5142 hsa136 hsa134 hsa135 hsa5138 hsa5139 hsa5153 hsa5136 hsa5137 621 | D00682 hsa5737 622 | D00298 hsa477 hsa476 hsa23439 hsa482 hsa483 hsa480 hsa481 hsa478 623 | D00525 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 624 | D00524 hsa1133 hsa1131 hsa1129 hsa1128 hsa1132 625 | D00831 626 | D00184 hsa11261 hsa5530 hsa5532 hsa5533 hsa5534 hsa5535 hsa63928 627 | D00521 hsa3269 hsa3757 628 | D00423 629 | D00523 hsa185 630 | D00522 hsa185 631 | D00618 hsa778 hsa779 hsa775 hsa776 632 | D00739 633 | D01747 hsa7155 hsa7153 634 | D02581 hsa4128 635 | D04790 636 | D03012 hsa6646 hsa8435 637 | D00749 hsa1723 638 | D02149 hsa154 hsa155 hsa150 hsa146 hsa152 hsa153 hsa148 hsa147 hsa151 639 | D02267 640 | D02261 641 | D00066 hsa5241 642 | D00067 hsa2100 hsa2099 643 | D00741 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 644 | D00316 hsa5916 hsa5914 hsa5915 645 | D01240 hsa477 hsa476 hsa23439 hsa482 hsa483 hsa480 hsa481 hsa478 646 | D02229 hsa8654 647 | D00317 648 | D00616 hsa778 hsa779 hsa775 hsa776 649 | D01161 hsa2100 hsa2099 650 | D01164 hsa3356 hsa1813 hsa3350 651 | D02588 hsa7201 652 | D01287 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 653 | D04040 hsa185 654 | D03643 hsa3156 655 | D03642 hsa6915 656 | D01513 hsa5743 hsa5742 657 | D00635 hsa153 658 | D03165 hsa1813 659 | D00065 660 | D03741 hsa5972 661 | D03742 hsa2902 hsa2903 hsa2904 hsa2905 hsa2906 662 | D03743 hsa5972 663 | D03745 hsa5972 664 | D03714 hsa5743 665 | D06106 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 666 | D00367 hsa6531 hsa6530 hsa6532 667 | D00364 hsa3269 668 | D00362 hsa1636 669 | D00740 hsa6261 hsa6263 hsa6262 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa8645 hsa6328 hsa6329 670 | D00246 hsa2908 671 | D04034 hsa1129 hsa1128 hsa150 hsa151 hsa152 hsa1131 hsa1132 hsa1133 hsa3356 hsa3357 hsa1813 hsa3269 hsa3358 672 | D00241 hsa9568 hsa2550 673 | D04031 hsa7150 674 | D02168 hsa7150 675 | D01768 676 | D02163 hsa1813 677 | D01765 hsa5743 hsa5742 678 | D03830 hsa778 hsa779 hsa775 hsa776 679 | D01767 hsa5743 hsa5742 680 | D02166 hsa7155 hsa7153 681 | D02769 hsa136 hsa134 hsa135 hsa140 682 | D01565 hsa5743 hsa5742 683 | D03260 hsa8654 684 | D00969 hsa5743 685 | D00968 hsa5743 hsa5742 686 | D02041 hsa170572 hsa285242 hsa9177 hsa200909 hsa3359 687 | D00965 hsa367 688 | D01866 hsa5743 hsa5742 689 | D00961 hsa367 690 | D01968 hsa2224 691 | D00963 hsa1588 692 | D00962 hsa2100 hsa2099 693 | D00095 hsa154 hsa155 hsa150 hsa146 hsa152 hsa153 hsa148 hsa147 hsa151 694 | D00094 hsa5916 hsa5914 hsa5915 695 | D00097 696 | D00900 697 | D00294 hsa6833 hsa3767 698 | D00560 hsa147 hsa1814 hsa1813 hsa146 hsa148 699 | D00563 hsa285242 hsa150 hsa151 hsa152 hsa170572 hsa3356 hsa3357 hsa200909 hsa9177 hsa3358 hsa3359 700 | D00562 hsa1733 hsa7173 hsa1734 701 | D00565 hsa5465 702 | D01915 hsa3156 703 | D00567 hsa5743 704 | D00566 hsa5743 hsa5742 705 | D00569 706 | D00568 hsa5743 707 | D00648 hsa3757 708 | D01264 hsa7155 hsa7153 709 | D00398 710 | D02357 hsa3356 hsa3352 hsa3358 hsa3350 hsa3351 711 | D03807 hsa231 712 | D00392 713 | D00642 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 714 | D02115 hsa1719 hsa200895 715 | D01269 hsa1131 716 | D00645 717 | D03580 hsa4988 718 | D00647 hsa3757 719 | D02335 720 | D05453 hsa2902 hsa2903 hsa2904 hsa2905 hsa2906 721 | D02237 722 | D04985 723 | D02234 hsa3269 724 | D04983 725 | D05458 726 | D00898 hsa2100 hsa2099 727 | D02367 hsa5241 728 | D00893 hsa3156 729 | D00892 hsa3156 730 | D00283 hsa1815 hsa147 hsa146 hsa148 hsa3363 hsa3356 hsa3350 hsa1813 hsa3358 731 | D00027 732 | D00021 733 | D00785 hsa4129 734 | D00786 hsa1312 735 | D00781 hsa1312 736 | D00029 737 | D02359 hsa154 738 | D02342 hsa153 739 | D02341 hsa5319 hsa8399 hsa50487 hsa84647 hsa391013 hsa5743 hsa5742 hsa26279 hsa30814 hsa5320 hsa81579 hsa5322 hsa64600 740 | D02340 hsa1813 741 | D02347 hsa6261 hsa6263 hsa6262 742 | D00418 hsa6833 hsa3767 743 | D00805 744 | D00415 hsa3352 hsa3351 745 | D00414 hsa240 746 | D00417 hsa5139 hsa5140 747 | D00416 748 | D00143 749 | D00142 hsa1719 hsa200895 750 | D00141 hsa5743 hsa5742 751 | D00947 752 | D03882 hsa240 753 | D03881 hsa153 754 | D03880 hsa153 755 | D01001 hsa43 756 | D05353 hsa495 hsa496 757 | D01132 hsa5916 hsa5914 hsa5915 758 | D03899 hsa7155 hsa7153 759 | D02556 760 | D00730 761 | D00732 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 762 | D05077 hsa6335 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 763 | D00735 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 764 | D00738 hsa6335 hsa6336 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 765 | D04292 hsa43 766 | D00829 767 | D06396 hsa170572 hsa3360 hsa285242 hsa200909 hsa9177 hsa3359 768 | D01799 hsa6833 769 | D02418 hsa43 770 | D00516 771 | D00329 hsa2568 hsa2561 hsa2557 hsa2556 hsa2555 hsa2554 hsa2562 hsa2563 hsa2559 hsa2558 hsa2566 hsa2567 hsa2564 hsa2565 hsa2560 hsa55879 772 | D00328 hsa2908 773 | D05740 hsa3352 hsa3351 774 | D00120 hsa5743 hsa5742 775 | D00322 776 | D00327 hsa367 777 | D00326 hsa6532 778 | D00325 hsa2908 779 | D00324 hsa2908 780 | D02721 hsa5739 781 | D00827 hsa5743 hsa5742 782 | D02725 hsa5732 783 | D02729 hsa43 hsa590 784 | D00638 hsa90134 hsa3752 hsa6335 hsa3757 hsa81033 hsa6326 hsa6334 hsa6331 hsa6323 hsa6328 hsa6329 785 | D01136 hsa5340 786 | D05312 hsa4988 787 | D01133 hsa5139 hsa5140 788 | D01211 789 | D00530 790 | D04966 hsa5563 hsa5562 791 | D00823 hsa6532 792 | -------------------------------------------------------------------------------- /database/pubchem_SIDs.txt: -------------------------------------------------------------------------------- 1 | 7847500 2 | 7847503 3 | 7847504 4 | 7847506 5 | 7847508 6 | 7847966 7 | 17397362 8 | 17397364 9 | 17396800 10 | 7847211 11 | 7847372 12 | 7847278 13 | 7848360 14 | 7847378 15 | 7847275 16 | 7848166 17 | 7847248 18 | 7848160 19 | 7847075 20 | 7847070 21 | 7847073 22 | 17397766 23 | 17397319 24 | 7847509 25 | 17397831 26 | 7848947 27 | 7847439 28 | 17397834 29 | 7847437 30 | 7847430 31 | 7847433 32 | 47207596 33 | 17397962 34 | 7847209 35 | 17397963 36 | 7847207 37 | 7849162 38 | 7847204 39 | 7847200 40 | 17397168 41 | 7847188 42 | 7847993 43 | 7847856 44 | 7847089 45 | 7848061 46 | 7847850 47 | 7847851 48 | 17397867 49 | 17397865 50 | 17397864 51 | 17397862 52 | 17397861 53 | 7847481 54 | 7847480 55 | 7847483 56 | 7847271 57 | 7847485 58 | 7847484 59 | 7847487 60 | 7847489 61 | 7847488 62 | 7847722 63 | 7847724 64 | 7847628 65 | 7847294 66 | 7847295 67 | 7847291 68 | 7847292 69 | 7847298 70 | 7847299 71 | 17398024 72 | 7848831 73 | 7848830 74 | 17397590 75 | 7849427 76 | 7849426 77 | 7849421 78 | 7849420 79 | 7848409 80 | 7848019 81 | 47206880 82 | 7848017 83 | 7848014 84 | 7848013 85 | 7848010 86 | 17397912 87 | 7847650 88 | 7847651 89 | 7847652 90 | 17397914 91 | 17397500 92 | 7847156 93 | 7847154 94 | 7847845 95 | 7848495 96 | 7847588 97 | 17397856 98 | 7848887 99 | 7847713 100 | 7848243 101 | 7848246 102 | 7849331 103 | 7849210 104 | 7848538 105 | 7849337 106 | 7848085 107 | 7848087 108 | 7848083 109 | 17397807 110 | 7848127 111 | 7848124 112 | 47205864 113 | 47208053 114 | 7847476 115 | 7847477 116 | 17397037 117 | 7847579 118 | 7847578 119 | 7847178 120 | 7847571 121 | 7847570 122 | 7847572 123 | 7847575 124 | 7849208 125 | 7847576 126 | 7847086 127 | 47207714 128 | 7847498 129 | 7848338 130 | 7848339 131 | 7848332 132 | 7847321 133 | 17396840 134 | 17397041 135 | 47206541 136 | 7847683 137 | 7847682 138 | 7847681 139 | 7847687 140 | 7847686 141 | 7847685 142 | 7847684 143 | 7848628 144 | 7847689 145 | 7847688 146 | 7847888 147 | 7847881 148 | 7849056 149 | 7847391 150 | 7847390 151 | 7847186 152 | 7847392 153 | 7847395 154 | 7847394 155 | 7847397 156 | 7847396 157 | 7847399 158 | 7848904 159 | 47206710 160 | 47206715 161 | 7848902 162 | 7849508 163 | 7848050 164 | 7848057 165 | 7847769 166 | 7848059 167 | 7848058 168 | 7848196 169 | 7848195 170 | 7849415 171 | 7847199 172 | 7847198 173 | 7847197 174 | 7848755 175 | 7847195 176 | 7847194 177 | 7847193 178 | 7848751 179 | 7848752 180 | 7848753 181 | 7847618 182 | 17397817 183 | 7847753 184 | 17397813 185 | 7847757 186 | 17397811 187 | 7847759 188 | 7847611 189 | 7847612 190 | 7847613 191 | 17396749 192 | 7847615 193 | 7847616 194 | 7847817 195 | 7847814 196 | 7847818 197 | 7848286 198 | 7848280 199 | 7849374 200 | 7847435 201 | 47207127 202 | 47207122 203 | 7848576 204 | 7847104 205 | 7847105 206 | 7847106 207 | 7847107 208 | 7847100 209 | 7848828 210 | 7847103 211 | 7847108 212 | 7847109 213 | 17396730 214 | 17396731 215 | 17396732 216 | 17396733 217 | 17396734 218 | 17396735 219 | 17397555 220 | 17396737 221 | 7847715 222 | 47206963 223 | 17397071 224 | 17397371 225 | 17397370 226 | 7847531 227 | 7847530 228 | 7847360 229 | 7847361 230 | 7847364 231 | 7847365 232 | 7847366 233 | 7847367 234 | 7847369 235 | 7848025 236 | 7848174 237 | 7848906 238 | 7848171 239 | 7848178 240 | 7847629 241 | 7848274 242 | 17396927 243 | 17396888 244 | 7847216 245 | 7847408 246 | 7847864 247 | 7849153 248 | 7847401 249 | 7847863 250 | 7847402 251 | 7849159 252 | 7847404 253 | 7847406 254 | 7849397 255 | 7848306 256 | 7848305 257 | 7849394 258 | 7848303 259 | 7847219 260 | 7847962 261 | 7848962 262 | 17397893 263 | 7847210 264 | 7849253 265 | 7847964 266 | 7847213 267 | 47206751 268 | 47207896 269 | 7848715 270 | 7847827 271 | 7847826 272 | 7847825 273 | 7847824 274 | 17397852 275 | 7847714 276 | 7847717 277 | 7847716 278 | 7847711 279 | 17397854 280 | 7847712 281 | 17397858 282 | 7847719 283 | 7847718 284 | 47205952 285 | 7849027 286 | 7849026 287 | 7849028 288 | 7847492 289 | 7847491 290 | 7848953 291 | 7848950 292 | 17397743 293 | 7847910 294 | 17397514 295 | 7848430 296 | 7848433 297 | 7849433 298 | 7849434 299 | 47206270 300 | 7847660 301 | 7847662 302 | 47206274 303 | 7847664 304 | 7847667 305 | 7847143 306 | 7847147 307 | 7848488 308 | 47207401 309 | 47206804 310 | 17396914 311 | 17397334 312 | 17396666 313 | 7849320 314 | 7847619 315 | 7848504 316 | 17397341 317 | 47207011 318 | 7847754 319 | 7848132 320 | 7847756 321 | 7848134 322 | 17397637 323 | 7847610 324 | 47205878 325 | 17397426 326 | 7849229 327 | 7847567 328 | 7847564 329 | 7847565 330 | 7847449 331 | 7847560 332 | 7847561 333 | 7847445 334 | 7847614 335 | 7847446 336 | 47207764 337 | 17396825 338 | 7847443 339 | 7847569 340 | 7847250 341 | 7847251 342 | 7847252 343 | 7847253 344 | 7847254 345 | 7847255 346 | 7847256 347 | 7848666 348 | 47206023 349 | 7849288 350 | 7848224 351 | 7847318 352 | 7848227 353 | 7847097 354 | 7847095 355 | 7849321 356 | 7847317 357 | 7848662 358 | 17397270 359 | 7847690 360 | 7847691 361 | 7847693 362 | 17398000 363 | 7847695 364 | 7847696 365 | 17396898 366 | 7847698 367 | 7847699 368 | 17398008 369 | 7848610 370 | 7848612 371 | 7847482 372 | 7849069 373 | 7849267 374 | 7847599 375 | 7849264 376 | 7847957 377 | 7847956 378 | 7847892 379 | 7849104 380 | 7847891 381 | 7847896 382 | 7847596 383 | 7847894 384 | 7847594 385 | 7847720 386 | 7847388 387 | 7847721 388 | 47206727 389 | 7847384 390 | 7847385 391 | 7848916 392 | 7847383 393 | 7847381 394 | 7847874 395 | 7847875 396 | 7847877 397 | 7847870 398 | 7849254 399 | 7847878 400 | 7847625 401 | 17397808 402 | 7847626 403 | 17397966 404 | 7847620 405 | 17397964 406 | 17397965 407 | 17397801 408 | 17397803 409 | 7848762 410 | 17397805 411 | 7847181 412 | 7848767 413 | 17397413 414 | 7847748 415 | 7847749 416 | 7847744 417 | 7847742 418 | 7847743 419 | 7847740 420 | 7847741 421 | 7849401 422 | 7849400 423 | 7849406 424 | 7848810 425 | 7849363 426 | 7849409 427 | 7849408 428 | 7849367 429 | 7848460 430 | 7847133 431 | 7847135 432 | 7847134 433 | 7847138 434 | 7848861 435 | 17396727 436 | 17396729 437 | 17397067 438 | 17396865 439 | 7847528 440 | 7847529 441 | 7848064 442 | 7847522 443 | 7847520 444 | 7847521 445 | 7847525 446 | 7847359 447 | 7848388 448 | 7848387 449 | 7847351 450 | 7849312 451 | 7849499 452 | 7849079 453 | 7848261 454 | 17396892 455 | 17396896 456 | 17397983 457 | 7847417 458 | 7847414 459 | 7847415 460 | 7847798 461 | 17398288 462 | 7847411 463 | 7847795 464 | 7847797 465 | 7847791 466 | 47207830 467 | 7849147 468 | 7847559 469 | 7849148 470 | 7847554 471 | 7847553 472 | 7849326 473 | 7847228 474 | 7848316 475 | 7849387 476 | 7849380 477 | 7849382 478 | 7847221 479 | 7847223 480 | 7849227 481 | 7848319 482 | 7847226 483 | 7847948 484 | 7847949 485 | 17397189 486 | 7847582 487 | 7849399 488 | 7848645 489 | 7848728 490 | 7848641 491 | 7847830 492 | 7847834 493 | 7847836 494 | 7849176 495 | 17397845 496 | 17397847 497 | 7847701 498 | 17397841 499 | 17397840 500 | 7847704 501 | 17397842 502 | 7848189 503 | 7847708 504 | 17397849 505 | 7849149 506 | 7849132 507 | 7849130 508 | 7849036 509 | 7849035 510 | 7849030 511 | 7849031 512 | 7848924 513 | 7847457 514 | 7848928 515 | 7849517 516 | 7847902 517 | 7847903 518 | 7848421 519 | 7848873 520 | 7848844 521 | 17397886 522 | 7849386 523 | 7847175 524 | 7847174 525 | 7847177 526 | 7847171 527 | 7847173 528 | 17397931 529 | 47206010 530 | 7848033 531 | 7848032 532 | 47206468 533 | 17397916 534 | 17397830 535 | 7847776 536 | 17397832 537 | 7847774 538 | 7847773 539 | 17397915 540 | 7847679 541 | 7847677 542 | 7847675 543 | 7847672 544 | 7847673 545 | 7847670 546 | 7847562 547 | 7849211 548 | 47206838 549 | 7849293 550 | 7847165 551 | 7849296 552 | 7847659 553 | 7848453 554 | 47207027 555 | 7848450 556 | 7848516 557 | 7848517 558 | 7849224 559 | 7848513 560 | 7848511 561 | 17397783 562 | 47206600 563 | 17397626 564 | 17396992 565 | 7847393 566 | 17397232 567 | 17397943 568 | 17396752 569 | 7847458 570 | 7847517 571 | 7847515 572 | 7847514 573 | 7847453 574 | 7847518 575 | 7847609 576 | 7847307 577 | 7847608 578 | 7847302 579 | 7847301 580 | 7847264 581 | 7847266 582 | 7847263 583 | 7848358 584 | 17397825 585 | 7848350 586 | 7848357 587 | 17397711 588 | 7847750 589 | 7847312 590 | 17398013 591 | 17398012 592 | 17398011 593 | 17398010 594 | 7848977 595 | 7848973 596 | 7849070 597 | 7849476 598 | 7847428 599 | 7847425 600 | 7847424 601 | 7847422 602 | 7847420 603 | 7849274 604 | 7847589 605 | 7849276 606 | 7847946 607 | 7847580 608 | 7847581 609 | 7849171 610 | 7847584 611 | 7847585 612 | 7847586 613 | 7847587 614 | 7848776 615 | 7847840 616 | 7848775 617 | 7848772 618 | 7847844 619 | 7847846 620 | 7848778 621 | 7847632 622 | 7847633 623 | 7847631 624 | 7847634 625 | 7847635 626 | 17397875 627 | 17397876 628 | 47206224 629 | 17397870 630 | 17397872 631 | 17397405 632 | 7847702 633 | 7847739 634 | 7849338 635 | 7847733 636 | 7847732 637 | 7847731 638 | 7847736 639 | 7847286 640 | 7847285 641 | 7847284 642 | 47207453 643 | 7847706 644 | 7847289 645 | 7847398 646 | 7849413 647 | 17397580 648 | 17396784 649 | 7849416 650 | 7849417 651 | 7849418 652 | 7849419 653 | 7847705 654 | 7848808 655 | 17398007 656 | 7848413 657 | 17397820 658 | 7848415 659 | 7847645 660 | 17397903 661 | 7847643 662 | 7848969 663 | 7847640 664 | 17397908 665 | 17397909 666 | 17397573 667 | 7848780 668 | 7847122 669 | 7847123 670 | 7847120 671 | 17396717 672 | 7847283 673 | 7848890 674 | 7849144 675 | 17396876 676 | 17398009 677 | 7848525 678 | 7847349 679 | 7849309 680 | 7848395 681 | 7847340 682 | 7848259 683 | 7847347 684 | 7847345 685 | 7847967 686 | 7848090 687 | 7849039 688 | 17397751 689 | 7847697 690 | 7849317 691 | 17397996 692 | 7847463 693 | 7847460 694 | 7847467 695 | 7847466 696 | 7847464 697 | 7847780 698 | 7847236 699 | 7847237 700 | 7847549 701 | 7847231 702 | 7849414 703 | 7847546 704 | 7849138 705 | 7847540 706 | 7847541 707 | 7847543 708 | 17397582 709 | 7848327 710 | 7847336 711 | 7849103 712 | 7849234 713 | 7849237 714 | 17397192 715 | 47207082 716 | 17397725 717 | 7848031 718 | 7847953 719 | 7848987 720 | 7848980 721 | 7848730 722 | 7848638 723 | 7847951 724 | 7847855 725 | 7849043 726 | 7849128 727 | 7849046 728 | 7848199 729 | 7848933 730 | 47206702 731 | 7847591 732 | 7847082 733 | 7848026 734 | 7848027 735 | 7848024 736 | 7847590 737 | 47206854 738 | 7848023 739 | 7848185 740 | 17396664 741 | 7848181 742 | 7848028 743 | 7848182 744 | 7847162 745 | 7847163 746 | 47206143 747 | 7848427 748 | 17397823 749 | 17397822 750 | 17396750 751 | 17396751 752 | 17397827 753 | 7847762 754 | 17397824 755 | 7847603 756 | 7847602 757 | 7847601 758 | 17396759 759 | 7847768 760 | 7847606 761 | 7847605 762 | 7847604 763 | 7847805 764 | 7847804 765 | 7847806 766 | 7847800 767 | 7847803 768 | 7849348 769 | 7849349 770 | 7847968 771 | 7848299 772 | 7848114 773 | 7848291 774 | 7848290 775 | 47207130 776 | 7848449 777 | 7849467 778 | 7849392 779 | 7847117 780 | 7847113 781 | 7847111 782 | 7847382 783 | 17396983 784 | 7847118 785 | 47206713 786 | 47205999 787 | 7847127 788 | 17397894 789 | 17397895 790 | 7848781 791 | 17397891 -------------------------------------------------------------------------------- /database/pubchem_SIDs_to_CIDs.txt: -------------------------------------------------------------------------------- 1 | SIDs$ CIDs 2 | 7847500 54454 3 | 7847503 4485 4 | 7847504 4497 5 | 7847506 3033637 6 | 7847508 383414 7 | 7847966 60855 8 | 17397362 5493444 9 | 17396800 33630 10 | 7847211 8955 11 | 7847372 16078 12 | 7847278 5381226 13 | 7848360 71405 14 | 7847378 23667301 15 | 7847275 5746 16 | 7848166 107979 17 | 7847248 5280723 18 | 7848160 163727 19 | 7847075 33032 20 | 7847070 5893 21 | 7847073 643975 22 | 17397766 2973 23 | 17397319 31101 24 | 7847509 5833 25 | 17397831 5360696 26 | 7848947 636397 27 | 7847439 5452 28 | 17397834 3086651 29 | 7847437 2153 30 | 7847430 3957 31 | 7847433 4020 32 | 47207596 41693 33 | 17397962 6420042 34 | 7847209 3715 35 | 17397963 36811 36 | 7847207 4114 37 | 7849162 6321303 38 | 7847204 3559 39 | 7847200 3825 40 | 17397168 166558 41 | 7847188 1548887 42 | 7847993 5281104 43 | 7847856 67356 44 | 7847089 6140 45 | 7848061 5824 46 | 7847850 26758 47 | 7847851 4659569 48 | 17397867 11954367 49 | 17397865 11954366 50 | 17397864 3033702 51 | 17397862 107994 52 | 17397861 62956 53 | 7847481 60857 54 | 7847480 60490 55 | 7847483 4197 56 | 7847271 5280965 57 | 7847485 5282381 58 | 7847484 4201 59 | 7847487 5362129 60 | 7847489 37542 61 | 7847488 3001055 62 | 7847722 4121 63 | 7847724 5560 64 | 7847628 657298 65 | 7847294 9433 66 | 7847295 2170 67 | 7847291 83786 68 | 7847292 2118 69 | 7847298 3698 70 | 7847299 657201 71 | 17398024 8366 72 | 7848831 6420040 73 | 7848830 54677971 74 | 17397590 66383 75 | 7849427 60750 76 | 7849426 40973 77 | 7849421 43815 78 | 7849420 4940 79 | 7848409 2329 80 | 7848019 229455 81 | 47206880 2735102 82 | 7848017 13109 83 | 7848014 6279 84 | 7848013 13109 85 | 7848010 441401 86 | 17397912 6419928 87 | 7847650 3385 88 | 7847651 55245 89 | 7847652 3397 90 | 17397914 6918197 91 | 17397500 156413 92 | 7847156 5754 93 | 7847154 864 94 | 7847845 31100 95 | 7848495 151114 96 | 7847588 2541 97 | 17397856 3033690 98 | 7848887 6215 99 | 7847713 71329 100 | 7848243 656583 101 | 7848246 610479 102 | 7849331 64640 103 | 7849210 5815 104 | 7848538 3395 105 | 7849337 104152 106 | 7848085 66259 107 | 7848087 5362376 108 | 7848083 6081 109 | 17397807 119828 110 | 7848127 104758 111 | 7848124 60837 112 | 47205864 21155870 113 | 47208053 3035241 114 | 7847476 4174 115 | 7847477 5717 116 | 17397037 9910986 117 | 7847579 4828 118 | 7847578 1775 119 | 7847178 122142 120 | 7847571 61100 121 | 7847570 4753 122 | 7847572 4763 123 | 7847575 91430 124 | 7849208 2083 125 | 7847576 4781 126 | 7847086 54670067 127 | 47207714 5362436 128 | 7847498 39147 129 | 7848338 443939 130 | 7848339 158550 131 | 7848332 216457 132 | 7847321 2585 133 | 17396840 4078 134 | 17397041 21850 135 | 47206541 15130 136 | 7847683 41114 137 | 7847682 62920 138 | 7847681 60496 139 | 7847687 5388961 140 | 7847686 5362123 141 | 7847685 62969 142 | 7847684 4499 143 | 7848628 5282183 144 | 7847689 54889 145 | 7847688 23667962 146 | 7847888 62857 147 | 7847881 441358 148 | 7849056 6918047 149 | 7847391 9642 150 | 7847390 11954221 151 | 7847186 156391 152 | 7847392 3386 153 | 7847395 3393 154 | 7847394 15209 155 | 7847397 3440 156 | 7847396 3394 157 | 7847399 3454 158 | 7848904 160024 159 | 47206710 14677 160 | 47206715 9034 161 | 7848902 163751 162 | 7849508 59694 163 | 7848050 54746 164 | 7848057 8307 165 | 7847769 5281077 166 | 7848059 441411 167 | 7848058 8246 168 | 7848196 4823 169 | 7848195 5381 170 | 7849415 2520 171 | 7847199 3117 172 | 7847198 3059 173 | 7847197 5280453 174 | 7848755 71764 175 | 7847195 54676228 176 | 7847194 3672 177 | 7847193 36462 178 | 7848751 1549120 179 | 7848752 444025 180 | 7848753 5663 181 | 7847618 2337 182 | 17397817 11954363 183 | 7847753 56801 184 | 17397813 60781 185 | 7847757 3182 186 | 17397811 159324 187 | 7847759 23706210 188 | 7847611 3763 189 | 7847612 42113 190 | 7847613 5206 191 | 17396749 82148 192 | 7847615 4943 193 | 7847616 4192 194 | 7847817 5281078 195 | 7847814 3899 196 | 7847818 5497196 197 | 7848286 60953 198 | 7848280 9051 199 | 7849374 445639 200 | 7847435 460612 201 | 47207127 4771 202 | 47207122 9795678 203 | 7848576 2097 204 | 7847104 936 205 | 7847105 311 206 | 7847106 243 207 | 7847107 6287 208 | 7847100 6274 209 | 7848828 5282193 210 | 7847103 145742 211 | 7847108 5997 212 | 7847109 6288 213 | 17396730 34521 214 | 17396731 44571 215 | 17396732 4235 216 | 17396733 3052775 217 | 17396734 60824 218 | 17396735 9373 219 | 17397555 11954344 220 | 17396737 4011 221 | 7847715 68540 222 | 47206963 158781 223 | 17397071 6435922 224 | 17397371 6450551 225 | 17397370 9869929 226 | 7847531 4634 227 | 7847530 4616 228 | 7847360 3019 229 | 7847361 2756 230 | 7847364 2724385 231 | 7847365 5311071 232 | 7847366 3100 233 | 7847367 23724775 234 | 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478 | 7847221 13769 479 | 7847223 439693 480 | 7849227 51082 481 | 7848319 2910 482 | 7847226 23677829 483 | 7847948 72734318 484 | 7847949 9556529 485 | 17397189 2266 486 | 7847582 6883 487 | 7849399 3964 488 | 7848645 1981 489 | 7848728 444020 490 | 7848641 4888 491 | 7847830 441351 492 | 7847834 115366 493 | 7847836 2733 494 | 7849176 23678981 495 | 17397845 10457278 496 | 17397847 71320 497 | 7847701 62937 498 | 17397841 54892 499 | 17397840 6441813 500 | 7847704 41022 501 | 17397842 107807 502 | 7848189 66250 503 | 7847708 64642 504 | 17397849 6917815 505 | 7849149 108173 506 | 7849132 10429215 507 | 7849130 2723754 508 | 7849036 159271 509 | 7849035 656631 510 | 7849030 121586 511 | 7849031 119442 512 | 7848924 5282451 513 | 7847457 6256 514 | 7848928 54690031 515 | 7849517 22502 516 | 7847902 5462346 517 | 7847903 3033053 518 | 7848421 68551 519 | 7848873 5147 520 | 7848844 5284472 521 | 17397886 54676038 522 | 7849386 11224 523 | 7847175 5282315 524 | 7847174 5280427 525 | 7847177 2244 526 | 7847171 444305 527 | 7847173 5757 528 | 17397931 60692 529 | 47206010 6918111 530 | 7848033 23681059 531 | 7848032 54677470 532 | 47206468 3762 533 | 17397916 3037122 534 | 17397830 5464201 535 | 7847776 15851 536 | 17397832 64922 537 | 7847774 5284583 538 | 7847773 3292 539 | 17397915 23724885 540 | 7847679 49659 541 | 7847677 13221 542 | 7847675 68546 543 | 7847672 71401 544 | 7847673 68552 545 | 7847670 20179 546 | 7847562 5852 547 | 7849211 656783 548 | 47206838 5360515 549 | 7849293 11954233 550 | 7847165 338 551 | 7849296 65328 552 | 7847659 3476 553 | 7848453 5807 554 | 47207027 985 555 | 7848450 443958 556 | 7848516 64119 557 | 7848517 27072 558 | 7849224 5282479 559 | 7848513 5282419 560 | 7848511 5282418 561 | 17397783 3058754 562 | 47206600 157920 563 | 17397626 167446 564 | 17396992 23683779 565 | 7847393 6446 566 | 17397232 5463984 567 | 17397943 3055 568 | 17396752 52542 569 | 7847458 5576 570 | 7847517 5358 571 | 7847515 5342 572 | 7847514 5359476 573 | 7847453 5556 574 | 7847518 5359 575 | 7847609 3226 576 | 7847307 2284 577 | 7847608 3562 578 | 7847302 2249 579 | 7847301 2247 580 | 7847264 5983 581 | 7847266 5280954 582 | 7847263 5362471 583 | 7848358 1547484 584 | 17397825 156422 585 | 7848350 117965 586 | 7848357 9270 587 | 17397711 9859832 588 | 7847750 35329 589 | 7847312 5281004 590 | 17398013 5281037 591 | 17398012 472335 592 | 17398011 204101 593 | 17398010 3034010 594 | 7848977 5282455 595 | 7848973 153751 596 | 7849070 110635 597 | 7849476 657348 598 | 7847428 5362118 599 | 7847425 53232 600 | 7847424 3676 601 | 7847422 5311221 602 | 7847420 3878 603 | 7849274 65348 604 | 7847589 3749 605 | 7849276 54900 606 | 7847946 68553 607 | 7847580 5311068 608 | 7847581 4510 609 | 7849171 23674731 610 | 7847584 3038 611 | 7847585 2720 612 | 7847586 10660 613 | 7847587 5405 614 | 7848776 157313 615 | 7847840 5070 616 | 7848775 23675769 617 | 7848772 23674745 618 | 7847844 2381 619 | 7847846 5281081 620 | 7848778 133264 621 | 7847632 16760658 622 | 7847633 2662 623 | 7847631 3339 624 | 7847634 5090 625 | 7847635 4754 626 | 17397875 11954368 627 | 17397876 107706 628 | 47206224 9651 629 | 17397870 11983193 630 | 17397872 71454 631 | 17397405 5328940 632 | 7847702 441325 633 | 7847739 3033332 634 | 7849338 69568 635 | 7847733 5965 636 | 7847732 23724780 637 | 7847731 5281068 638 | 7847736 5741 639 | 7847286 1989 640 | 7847285 1986 641 | 7847284 1983 642 | 47207453 5152 643 | 7847706 36708 644 | 7847289 2022 645 | 7847398 3446 646 | 7849413 5440 647 | 17397580 189729 648 | 17396784 5311064 649 | 7849416 9681 650 | 7849417 4171 651 | 7849418 33572 652 | 7849419 68617 653 | 7847705 21467 654 | 7848808 3151 655 | 17398007 11954378 656 | 7848413 19648 657 | 17397820 26757 658 | 7848415 5282415 659 | 7847645 44561 660 | 17397903 11954370 661 | 7847643 448537 662 | 7848969 30751 663 | 7847640 2145 664 | 17397908 77991 665 | 17397909 132228 666 | 17397573 3033824 667 | 7848780 54384 668 | 7847122 6021 669 | 7847123 967 670 | 7847120 6049 671 | 17396717 11954253 672 | 7847283 41774 673 | 7848890 2161 674 | 7849144 5282474 675 | 17396876 119607 676 | 17398009 6444692 677 | 7848525 5282421 678 | 7847349 2818 679 | 7849309 441258 680 | 7848395 5282408 681 | 7847340 2769 682 | 7848259 13290219 683 | 7847347 2803 684 | 7847345 2796 685 | 7847967 23667642 686 | 7848090 11953898 687 | 7849039 123631 688 | 17397751 444031 689 | 7847697 91725 690 | 7849317 656846 691 | 17397996 48040 692 | 7847463 5593 693 | 7847460 5584 694 | 7847467 1349907 695 | 7847466 60846 696 | 7847464 60772 697 | 7847780 23724781 698 | 7847236 6253 699 | 7847237 23663959 700 | 7847549 62882 701 | 7847231 10133 702 | 7849414 5509 703 | 7847546 6014 704 | 7849138 54405 705 | 7847540 4909 706 | 7847541 4911 707 | 7847543 66068 708 | 17397582 16129675 709 | 7848327 62770 710 | 7847336 2726 711 | 7849103 656664 712 | 7849234 121587 713 | 7849237 10917 714 | 17397192 9571003 715 | 47207082 4725 716 | 17397725 6726 717 | 7848031 64747 718 | 7847953 23663992 719 | 7848987 656609 720 | 7848980 23663985 721 | 7848730 5362115 722 | 7848638 10649 723 | 7847951 60822 724 | 7847855 667467 725 | 7849043 9574101 726 | 7849128 656677 727 | 7849046 21121303 728 | 7848199 5526 729 | 7848933 71177 730 | 47206702 4091 731 | 7847591 5910 732 | 7847082 124886 733 | 7848026 60198 734 | 7848027 3902 735 | 7848024 2375 736 | 7847590 5831 737 | 47206854 50294 738 | 7848023 2187 739 | 7848185 3673 740 | 17396664 37392 741 | 7848181 122824 742 | 7848028 4493 743 | 7848182 443873 744 | 7847162 444795 745 | 7847163 5816 746 | 47206143 5790 747 | 7848427 38911 748 | 17397823 170321 749 | 17397822 6446650 750 | 17396750 3748 751 | 17396751 3759 752 | 17397827 60594 753 | 7847762 3373 754 | 17397824 71355 755 | 7847603 5284627 756 | 7847602 3331 757 | 7847601 5665 758 | 17396759 6917793 759 | 7847768 3284 760 | 7847606 11693 761 | 7847605 3291 762 | 7847604 5734 763 | 7847805 5795 764 | 7847804 73957 765 | 7847806 8695 766 | 7847800 68304 767 | 7847803 66070 768 | 7849348 247839 769 | 7849349 23706216 770 | 7847968 5018304 771 | 7848299 216322 772 | 7848114 5265 773 | 7848291 27522 774 | 7848290 185462 775 | 47207130 23724987 776 | 7848449 65326 777 | 7849467 23669229 778 | 7849392 54675768 779 | 7847117 938 780 | 7847113 60961 781 | 7847111 5936 782 | 7847382 5282375 783 | 17396983 9907628 784 | 7847118 493570 785 | 47206713 876 786 | 47205999 23724898 787 | 7847127 6047 788 | 17397894 5278 789 | 17397895 5724 790 | 7848781 4237 791 | 17397891 157838 792 | -------------------------------------------------------------------------------- /database/target_kegg_uniprot.txt: -------------------------------------------------------------------------------- 1 | KEGG_TARGET_ID$UNIPROT_TARGET_ID 2 | hsa3815 A0A024RDA0 P10721 3 | hsa3817 A0A024R4J4 B4DU77 A0A024R4N3 P20151 4 | hsa3816 P06870 5 | hsa3818 P03952 6 | hsa5169 O14638 Q308M7 7 | hsa56660 Q9HB15 8 | hsa11072 Q6FI36 O95147 9 | hsa5737 P43088 10 | hsa6098 P08922 11 | hsa5732 P43116 12 | hsa5733 O00325 P43115 13 | hsa5731 P34995 14 | hsa5045 A0A024RC70 P09958 15 | hsa3269 P35367 16 | hsa6096 Q58EY0 17 | hsa6097 P51449 Q6I9R9 F1D8P6 18 | hsa6095 P35398 19 | hsa2550 Q8IW08 Q9UBS5 Q5SUJ9 20 | hsa2557 P48169 X5D7F5 21 | hsa2556 P34903 22 | hsa2555 P47869 A0A024R9X6 23 | hsa2554 P14867 A8K177 24 | hsa34 B4DJE7 P11310 B7Z9I1 Q5T4U5 Q5HYG7 25 | hsa35 E5KSD5 B4DUH1 P16219 E9PE82 26 | hsa2559 Q16445 27 | hsa2558 P31644 28 | hsa30 A0A024R2M6 P09110 29 | hsa31 Q7Z5W8 Q13085 B2ZZ90 A0A024R0Y2 30 | hsa32 O00763 31 | hsa476 P05023 32 | hsa29920 A0A087WTV6 Q96C36 33 | hsa472 Q13315 A0A024R3C7 34 | hsa8836 Q92820 35 | hsa64902 Q9BYV1 36 | hsa51645 Q9Y3C6 A0A024RCX8 37 | hsa154 P07550 38 | hsa155 A8KAG8 P13945 39 | hsa5168 Q13822 40 | hsa150 P08913 41 | hsa151 A2RUS0 P18089 42 | hsa152 P18825 Q4W594 43 | hsa153 P08588 44 | hsa3741 P22460 45 | hsa54657 P22310 46 | hsa199974 Q86W10 47 | hsa59340 Q9H3N8 B2KJ49 48 | hsa3551 O14920 49 | hsa4920 Q01974 50 | hsa4921 A0A024R906 Q16832 51 | hsa1384 P43155 52 | hsa9563 O95479 R4GMU1 53 | hsa84695 P58215 54 | hsa10935 P30048 55 | hsa169522 Q8TDN2 56 | hsa94009 57 | hsa1675 P00746 58 | hsa658 O00238 A8KAE3 59 | hsa5049 V9HW44 P68402 60 | hsa50940 Q9HCR9 61 | hsa23236 Q9NQ66 62 | hsa6833 Q09428 63 | hsa5465 F1D8S4 Q07869 64 | hsa5468 D2KUA6 P37231 65 | hsa7006 P42680 66 | hsa23239 O60346 67 | hsa3174 Q14541 F1D8Q4 68 | hsa1553 Q16696 69 | hsa27034 Q9UKU7 70 | hsa1551 P24462 71 | hsa27032 P98194 B4E295 B4E2Q0 72 | hsa1557 P33261 73 | hsa1555 P20813 74 | hsa1558 B7Z1F5 P10632 75 | hsa1559 P11712 S5RV20 76 | hsa2673 Q06210 77 | hsa2677 P38435 78 | hsa8645 A0A024RD32 O95279 79 | hsa5914 Q6I9R7 P10276 A8K840 F1D8N9 A8MUP8 80 | hsa5739 P43119 81 | hsa81033 B4DKC0 J9JID4 Q9H252 B4DPJ3 82 | hsa9971 Q96RI1 F1DAL1 B6ZGS9 83 | hsa54600 Q5DSZ5 O60656 84 | hsa1589 Q08AG9 P08686 Q16874 85 | hsa27012 Q6PIU1 86 | hsa10060 O60706 A0A024RAV7 87 | hsa4025 P22079 88 | hsa7174 P29144 Q5VZU9 89 | hsa3738 P22001 90 | hsa3739 P22459 91 | hsa4023 P06858 92 | hsa3735 Q15046 93 | hsa3736 Q09470 94 | hsa3737 P16389 95 | hsa8989 O75762 96 | hsa136 P29275 97 | hsa3355 P30939 98 | hsa1852 B2RAL9 Q99956 99 | hsa7075 P35590 B4DTW8 100 | hsa1850 Q13202 101 | hsa51305 A0A024R9H3 Q9NPC2 102 | hsa134 B7Z1L9 B7Z379 P30542 103 | hsa9388 Q9Y5X9 A0A024R2B5 104 | hsa5605 P36507 105 | hsa10747 O00187 106 | hsa6531 Q01959 107 | hsa36 P45954 108 | hsa10667 O95363 109 | hsa10846 Q9Y233 110 | hsa6799 E9PKW4 P50226 111 | hsa444 A0A0A0MSK8 B7ZM96 B4DQ07 Q12797 112 | hsa6532 P31645 B2R7Y7 113 | hsa5598 Q13164 A0A024QZ20 114 | hsa128853 Q9H1R2 115 | hsa5029 P41231 116 | hsa2185 Q14289 117 | hsa326625 Q96EY8 118 | hsa2950 P09211 V9HWE9 119 | hsa3781 Q9H2S1 120 | hsa3780 Q92952 121 | hsa3783 O15554 122 | hsa3782 Q9UGI6 A0A0A0MQW9 A0A087WYJ0 123 | hsa3785 Q53Y30 O43526 124 | hsa3784 Q96AI9 P51787 125 | hsa3787 Q96KK3 A2RUL8 126 | hsa3786 O43525 127 | hsa6751 Q86SW9 P30872 128 | hsa3788 Q9ULS6 129 | hsa6753 P32745 130 | hsa6752 P30874 131 | hsa6755 P35346 132 | hsa51601 Q9Y234 133 | hsa55584 Q9UGM1 134 | hsa8973 Q15825 135 | hsa8972 E7ER45 O43451 136 | hsa5167 P22413 137 | hsa2356 Q05932 138 | hsa4329 Q02252 A0A024R6G4 139 | hsa9127 O15547 140 | hsa216 P00352 V9HW83 141 | hsa190 P51843 F1D8P4 142 | hsa191 P23526 143 | hsa219 P30837 144 | hsa218 P30838 Q6PKA6 145 | hsa116443 Q8TCU5 146 | hsa7957 O95278 147 | hsa5138 Q8IW54 O00408 148 | hsa116444 O60391 149 | hsa116447 Q969P6 E5KMK7 B4DYD2 150 | hsa7225 Q9Y210 151 | hsa5136 B7Z7A2 P54750 B7Z226 Q9Y633 152 | hsa5137 Q14123 A0A0A0MS69 153 | hsa4552 Q9UBK8 154 | hsa1633 F5CTF3 P27707 155 | hsa695 Q5JY90 Q06187 156 | hsa1636 D3DU13 P12821 B4DKH4 157 | hsa7453 P23381 A0A024R6K8 158 | hsa1733 P49895 159 | hsa1734 Q9HCP7 A8K845 Q92813 160 | hsa1735 P55073 Q86TU3 161 | hsa327 P13798 A0A024R2U9 162 | hsa2562 X5DQY4 B2RCW8 P28472 163 | hsa26279 Q9UNK4 A0A087WZT4 164 | hsa11280 Q9UI33 165 | hsa6256 P19793 Q6P3U7 F1D8Q5 166 | hsa6257 Q5STP9 B7Z7J5 P28702 167 | hsa3990 A6H8L5 P11150 168 | hsa3991 Q05469 A8K8W7 169 | hsa2900 A6H8K8 B2RAP6 Q16099 170 | hsa5550 P48147 B2RAH7 171 | hsa2901 Q16478 172 | hsa2548 P10253 173 | hsa4191 A0A024R4K3 B3KTM1 G3XAL0 P40926 174 | hsa4190 V9HWF2 P40925 175 | hsa2567 Q99928 176 | hsa1594 O15528 177 | hsa1595 Q16850 178 | hsa1593 Q02318 179 | hsa109 A0A0A0MSC1 O60266 180 | hsa108 Q08462 Q71UM8 181 | hsa107 Q8NFM5 C9J1J0 Q59FG4 Q08828 182 | hsa4353 P05164 183 | hsa1910 P24530 A0A024R645 A0A024R638 184 | hsa100 F5GWI4 P00813 185 | hsa1024 P49336 186 | hsa1025 A0A024R880 P50750 187 | hsa1022 P50613 188 | hsa1020 Q00535 A0A090N7W4 189 | hsa1021 Q00534 190 | hsa260293 Q8N118 191 | hsa1181 P51788 192 | hsa349565 Q96T66 193 | hsa5650 B4DHX9 A0A024R4H6 P49862 194 | hsa28227 Q9Y5P8 195 | hsa7525 P07947 196 | hsa10901 Q9BTZ2 F5GWZ1 197 | hsa4953 B4DXF8 P11926 198 | hsa57053 Q9GZZ6 C4IXS7 199 | hsa1815 P21917 200 | hsa3772 Q99712 201 | hsa3773 K7EJR9 Q9NPI9 A8K434 Q8N538 202 | hsa3775 O00180 A0A024R3T2 203 | hsa3776 Q6ZW95 U3N834 O95069 U3N6F0 204 | hsa3778 Q59FH2 S4R453 A0A0A0MRR0 D5MRH1 A0A087WZL8 B7ZMF5 Q12791 205 | hsa3779 Q16558 206 | hsa54490 Q9BY64 207 | hsa1018 Q00526 208 | hsa4759 Q9Y3R4 209 | hsa1200 O14773 210 | hsa3141 P50747 211 | hsa9052 Q8NFJ5 212 | hsa23430 Q9BZJ3 213 | hsa6898 P17735 214 | hsa1890 B2RBL3 E5KRG5 P19971 215 | hsa3028 Q99714 216 | hsa5351 A0A024R4H0 Q02809 217 | hsa151531 O95045 218 | hsa6324 Q07699 219 | hsa1445 P41240 B2R6Q4 A8K3B6 220 | hsa6326 Q99250 221 | hsa6323 P35498 222 | hsa1562 P33260 Q7Z348 223 | hsa6241 P31350 224 | hsa6240 P23921 225 | hsa6328 Q9NY46 Q9C007 226 | hsa6329 P35499 227 | hsa1565 P10635 Q5Y7H2 C1ID52 228 | hsa2913 A4D1D0 Q14832 229 | hsa2912 Q86YG3 Q14416 230 | hsa2911 Q59HC2 Q13255 231 | hsa2917 B9EGG9 B2R693 Q14831 Q59G95 232 | hsa2916 O15303 233 | hsa2915 A8K5P7 P41594 234 | hsa2914 B7ZLU9 Q14833 A8K0J8 A1L4F9 235 | hsa54677 Q9UKG9 236 | hsa2918 O00222 237 | hsa5530 Q08209 238 | hsa5531 A0A024R625 P60510 239 | hsa5532 B7Z781 P16298 240 | hsa5533 P48454 241 | hsa5536 A0A024R0Q7 P53041 242 | hsa5537 O00743 A0A024R861 243 | hsa5538 P50897 244 | hsa10056 Q9NSD9 245 | hsa6716 P31213 246 | hsa11330 Q99895 247 | hsa11238 A0A024RBW9 Q9Y2D0 248 | hsa6713 Q14534 Q9UNR6 Q5HYI4 249 | hsa1806 Q12882 250 | hsa1800 P16444 251 | hsa2582 Q14376 252 | hsa1803 P27487 253 | hsa142679 Q8WTR2 254 | hsa170572 Q8WXA8 255 | hsa51727 P30085 B2R6S5 256 | hsa3358 P28335 K9J958 257 | hsa1360 P15086 258 | hsa1363 P16870 259 | hsa1813 A0A024R3I6 A0A024R3C5 P14416 260 | hsa27092 A0A024R8J8 Q9UBN1 261 | hsa27094 Q9NPA1 262 | hsa56479 Q9NR82 263 | hsa29941 Q6P5Z2 A0A024R8D6 264 | hsa10279 Q9NQE7 265 | hsa56659 Q9HB14 266 | hsa51802 Q9NY37 267 | hsa313 P28039 A0A087WVT3 268 | hsa5743 P35354 269 | hsa5742 P23219 A0A087X296 270 | hsa5740 Q16647 271 | hsa5747 Q59GM6 Q658W2 Q05397 E7ESA6 272 | hsa3295 E7EWE5 B3KSP2 B2R659 P51659 G5E9S2 273 | hsa495 Q658V6 P20648 274 | hsa2822 P80108 275 | hsa55879 Q9UN88 276 | hsa3290 X5D2L1 P28845 277 | hsa49 P10323 278 | hsa7498 P47989 279 | hsa57657 Q9P1Z3 280 | hsa5624 P04070 281 | hsa40 Q16515 282 | hsa43 P22303 283 | hsa1147 O15111 284 | hsa8843 P49019 285 | hsa2260 P11362 286 | hsa2263 P21802 D3DRD5 D2CGD1 287 | hsa367 P10275 F1D8N5 288 | hsa2264 J3KPQ0 B4DVP5 P22455 289 | hsa29785 Q96SQ9 290 | hsa635 Q93088 V9HWA4 291 | hsa4311 P08473 292 | hsa140 H6VQ59 P33765 Q6P2N6 293 | hsa147 P35368 294 | hsa146 P25100 B0ZBE0 295 | hsa148 B0ZBD3 P35348 296 | hsa2766 P36959 297 | hsa93650 Q9BZG2 298 | hsa223 P49189 B9EKV4 299 | hsa9177 O95264 300 | hsa224 P51648 301 | hsa2045 Q15375 302 | hsa2044 B7ZKW7 F8W9W0 Q59FT4 F8VP57 B7ZKJ3 P54756 303 | hsa2047 P54762 304 | hsa2046 P29322 305 | hsa2041 P21709 306 | hsa2043 Q58F15 E9PG71 P54764 A0A024R452 307 | hsa2042 Q6P4R6 C9JXA2 P29320 308 | hsa4915 A0A024R230 Q548C2 Q16620 Q5VWE5 309 | hsa4914 P04629 X5DR71 310 | hsa4916 X5DNW6 Q96CY4 Q16288 X5D7M5 X5D2R1 311 | hsa85313 Q8WUA2 312 | hsa2048 P29323 313 | hsa225689 Q8TD08 314 | hsa10941 D6RFW5 Q9Y4X1 315 | hsa5294 A8K9G9 A0A024R720 P48736 316 | hsa657 P36894 317 | hsa2160 P03951 318 | hsa2161 P00748 Q8IZZ5 319 | hsa1129 P08172 Q6SL59 A4D1Q0 Q86SJ1 Q6SL56 320 | hsa1128 Q53XZ3 P11229 321 | hsa1080 P13569 A0A024R730 322 | hsa50484 Q7LG56 323 | hsa50487 Q9NZ20 324 | hsa134510 Q8WVY7 325 | hsa8622 B3KN77 O95263 326 | hsa23630 Q5JWV7 Q9UJ90 327 | hsa6534 Q99884 328 | hsa23632 Q9ULX7 A8K3J4 329 | hsa135 A8K1F6 X5DNB4 P29274 B3KVQ4 330 | hsa132 P55263 331 | hsa6530 P23975 A0A024R6T9 332 | hsa5499 P62136 333 | hsa131 P40394 334 | hsa5496 O15355 Q6IAU5 335 | hsa5495 O75688 336 | hsa5494 A0A024R6A5 B2R8E4 P35813 337 | hsa64802 B1AN62 Q9HAN9 A0A024R4E1 338 | hsa5319 P04054 339 | hsa22954 A0A024R843 Q13049 340 | hsa22953 Q9UBL9 Q32MC3 341 | hsa64805 A8K7T1 Q9H244 342 | hsa5310 P98161 343 | hsa196883 Q86TZ7 Q8NFM4 344 | hsa4986 P41145 A0A087WU84 345 | hsa1019 P11802 A0A024RBB6 346 | hsa4985 P41143 347 | hsa1017 B4DDL9 A0A024RB77 A0A024RB10 P24941 E7ESI2 348 | hsa4860 P00491 V9HWH6 349 | hsa4988 L0E130 P35372 G8XRH5 B8K2Q5 G8XRH4 350 | hsa8654 G5E9C5 O76074 351 | hsa10720 O75310 352 | hsa1234 P51681 Q38L21 353 | hsa54207 P57789 354 | hsa8435 O75908 355 | hsa1844 Q05923 356 | hsa3708 Q14643 B4DER3 B4DGH1 Q59H91 357 | hsa2890 Q59GL5 P42261 358 | hsa10269 O75844 359 | hsa3702 Q08881 360 | hsa112 O43306 361 | hsa3577 P25024 362 | hsa1848 A0A024RBC1 Q16828 Q53GP9 363 | hsa1849 Q16829 364 | hsa5406 P16233 365 | hsa5407 P54315 366 | hsa27115 A1E5M1 Q9NP56 367 | hsa1843 B4DU40 P28562 368 | hsa5408 P54317 369 | hsa1845 P51452 370 | hsa1846 Q13115 371 | hsa1847 Q16690 372 | hsa4486 B4E058 Q04912 373 | hsa1241 Q15722 374 | hsa4482 Q9UJ68 375 | hsa2099 Q9UBT1 A8KAF4 G4XH65 P03372 376 | hsa5147 O43924 B8ZZK5 Q6IB24 377 | hsa6652 Q00796 378 | hsa306 P12429 379 | hsa590 D3DNN4 P06276 380 | hsa51292 Q9P2T1 Q6PKC0 H0YNJ6 381 | hsa5031 A0A024R5I9 Q15077 382 | hsa5030 P51582 383 | hsa3067 P19113 384 | hsa5032 Q96G91 385 | hsa2100 Q7LCB3 F1D8N3 Q0PTK2 Q92731 386 | hsa3416 P14735 387 | hsa1909 P25101 388 | hsa150290 Q8NEJ0 A0A024R1L2 389 | hsa114 Q4F7X0 P40145 390 | hsa11202 A0A0A0MQY9 O60259 391 | hsa2103 O95718 392 | hsa2342 P49356 393 | hsa84812 Q9BRC7 394 | hsa2224 P14324 A0A087X090 395 | hsa3790 Q9BQ31 396 | hsa3791 A0A024RD88 P35968 397 | hsa3274 P25021 398 | hsa51205 X5D319 X5D289 Q9NPH0 A0A0A0MS36 399 | hsa115 O60503 400 | hsa51207 A0A024QZR6 Q9UII6 Q6B8I1 401 | hsa771 B3KUB4 O43570 402 | hsa9132 P56696 B3KQH8 403 | hsa773 Q9NS89 O00555 A0A087WW63 B5TYJ1 404 | hsa774 Q00975 405 | hsa775 Q59GU3 A0A0A0MR67 Q5V9X9 Q13936 A0A0A0MSA1 406 | hsa776 Q01668 Q59GD8 407 | hsa777 Q59FG1 Q15878 408 | hsa778 O60840 409 | hsa779 Q13698 B1ALM3 410 | hsa3932 P06239 411 | hsa5724 A8K7N8 P25105 412 | hsa10549 Q13162 V9HW63 413 | hsa4548 Q99707 A0A0A6YYK0 414 | hsa4543 P48039 415 | hsa55800 Q9NY72 A0A024R3H7 416 | hsa60598 Q9H427 417 | hsa1621 P09172 418 | hsa1990 Q9UNI1 419 | hsa1991 P08246 420 | hsa13 P22760 421 | hsa10 P11245 A4Z6T7 422 | hsa84171 Q96JB6 423 | hsa57665 Q9HBH5 Q53RX3 424 | hsa18 P80404 X5D8S1 425 | hsa353 P07741 426 | hsa284541 Q5TCH5 Q5TCH4 A0A087WZX9 427 | hsa354 P07288 Q546G3 428 | hsa2534 P06241 429 | hsa38 P24752 430 | hsa5023 P51575 431 | hsa1816 P21918 432 | hsa983 I6L9I5 P06493 B7Z3D6 A0A024QZJ8 433 | hsa128 Q6IRT1 P11766 434 | hsa59284 P62955 435 | hsa270 P23109 436 | hsa785 E7EN11 E7DBM8 O00305 437 | hsa93589 Q7Z3S7 438 | hsa784 P54284 439 | hsa9955 Q9Y663 440 | hsa4942 P04181 441 | hsa1056 O75612 B4DSX9 X6R868 Q86SR3 442 | hsa111 B7Z2C7 O95622 B3KWA8 443 | hsa7363 P06133 444 | hsa2261 X5D2G8 P22607 Q0IJ44 445 | hsa11254 B2R8J1 Q9UN76 446 | hsa7367 O75795 447 | hsa7366 P54855 B7ZW53 448 | hsa7365 P36537 449 | hsa7364 P16662 450 | hsa185 A0A0A0MSE3 Q53YY0 P30556 D3DNG8 Q6NUP5 451 | hsa113 F5H4D1 Q86YI0 P51828 B3KSJ0 452 | hsa9424 Q9Y257 B2RDS2 453 | hsa30814 Q9NZK7 454 | hsa7535 P43403 455 | hsa57105 Q5KU17 Q9NS75 456 | hsa3376 P41252 Q7L4K8 Q6P0M4 457 | hsa189 P21549 458 | hsa2158 P00740 459 | hsa51 Q15067 460 | hsa3749 H7BZ66 Q03721 461 | hsa3748 Q14003 462 | hsa3745 Q14721 463 | hsa3744 Q16322 464 | hsa3747 A0A024RBF2 A0A024RBE3 A0A024RBA5 Q96PR1 465 | hsa3746 Q3KNS8 P48547 466 | hsa1178 Q05315 467 | hsa3743 Q96RP8 468 | hsa3742 P17658 469 | hsa6915 P21731 Q05C92 Q0VAB0 470 | hsa9601 A0A090N8Y2 P13667 471 | hsa23704 A5H1P5 Q8WWG9 472 | hsa3156 A0A024RAP2 P04035 473 | hsa2569 P24046 474 | hsa377677 Q8N1Q1 475 | hsa64850 Q8TBG4 E7ENR6 476 | hsa5444 P27169 477 | hsa5445 Q15165 J3QT77 478 | hsa93107 Q32MC1 Q8TDN1 Q547S7 479 | hsa5447 P16435 480 | hsa6258 P48443 F1D8Q7 A0A087WZ88 F1T097 481 | hsa6259 A0A096LNL3 P34925 A0A087WUK1 Q8WTZ8 Q59FQ5 482 | hsa125 P00325 V9HW50 483 | hsa1571 P05181 484 | hsa1572 P24903 485 | hsa1573 P51589 486 | hsa1576 Q6GRK0 P08684 487 | hsa1577 B7Z3P6 P20815 488 | hsa558 M0R0W6 P30530 489 | hsa10008 Q9Y6H6 Q6IAE6 490 | hsa2902 Q05586 Q5VSF9 Q59GW0 491 | hsa2903 Q547U9 Q12879 Q59EW6 492 | hsa2904 Q13224 493 | hsa2905 Q8IW23 H0Y2V8 O15398 Q14957 494 | hsa2906 Q59G17 O15399 495 | hsa2908 P04150 F1D8N4 E5KQF6 E5KQF5 496 | hsa2895 O43424 497 | hsa552 P37288 X5D2B0 498 | hsa554 P30518 499 | hsa5916 A8K3H3 F1D8P1 P13631 B7Z4B4 500 | hsa1814 X5D2G4 E9PCM4 A1A4V4 P35462 A8K8E4 501 | hsa10369 Q9Y698 502 | hsa5501 P36873 A0A024RBP2 503 | hsa5500 P62140 V9HW04 504 | hsa57016 O60218 505 | hsa5915 Q3SB16 Q5QHG3 F1D8S6 P10826 Q86UC5 506 | hsa58190 A0A024R461 Q9GZU7 507 | hsa7155 Q59H80 Q02880 508 | hsa7156 B4DSJ0 Q13472 509 | hsa1579 Q02928 V9GZ77 510 | hsa7150 P11387 511 | hsa7153 P11388 512 | hsa1376 P23786 513 | hsa1374 A0A024R5F4 Q8WZ48 P50416 B2RAQ8 514 | hsa80339 Q9NST1 515 | hsa6263 Q15413 516 | hsa5149 Q13956 517 | hsa5148 P18545 518 | hsa1268 V5KA96 P21554 S5TLS4 519 | hsa7294 P42681 520 | hsa5145 P16499 521 | hsa5144 Q08499 522 | hsa55512 A8K0T6 Q9NY59 523 | hsa7298 P04818 Q53Y97 524 | hsa5141 P27815 525 | hsa55515 Q96FT7 526 | hsa5143 P78505 O43849 Q32MM7 Q7KYS4 Q08493 527 | hsa5142 Q07343 Q59GM8 X5DNX5 Q68CX5 X5DR82 528 | hsa1956 P00533 529 | hsa5754 Q13308 530 | hsa5547 B7Z7Q6 P42785 A0A024R5L0 531 | hsa25796 O95336 532 | hsa10461 Q12866 533 | hsa4048 P09960 534 | hsa3283 P14060 535 | hsa781 E7ERK3 P54289 Q9UIU0 536 | hsa780 A0A024RCL1 Q96T61 Q96T62 A0A024RCQ1 A0A0A0MSX3 Q08345 A0A024RCJ0 537 | hsa783 Q59H42 Q5VVH1 Q08289 538 | hsa782 Q02641 539 | hsa5596 K7ELV1 P31152 B4DEW2 540 | hsa5597 Q16659 541 | hsa11255 A0A0A0MR48 Q9Y5N1 542 | hsa786 Q06432 543 | hsa52 Q59EH3 P24666 544 | hsa53 E9PQY3 B7Z6T8 B7Z8T9 B7Z552 B7Z4Z2 B7Z7D2 E9PHY0 P11117 B7Z6L8 545 | hsa50 Q99798 546 | hsa5599 A1L4K2 P45983 547 | hsa2570 P28476 548 | hsa54 P13686 A0A024R7F8 549 | hsa55 P15309 550 | hsa8911 Q9P0X4 551 | hsa8913 O43497 552 | hsa8912 B3KQH9 O95180 553 | hsa50801 Q2YDA1 Q9NYG8 A0A024R5C7 554 | hsa8854 O94788 555 | hsa8856 F1D8P9 O75469 556 | hsa84618 Q9BXI3 557 | hsa2597 P04406 V9HVZ4 558 | hsa3906 P00709 A0A080YV01 559 | hsa2595 H3BN99 B4DWC5 Q8TET4 E7EWB6 560 | hsa4306 B0ZBF6 P08235 561 | hsa6010 P08100 562 | hsa239 P18054 563 | hsa238 Q9UM73 B6D4Y2 564 | hsa7297 A0A024R7E4 P29597 565 | hsa222545 Q5T6X5 566 | hsa9992 Q9Y6J6 567 | hsa10188 B3KXJ4 C9J1X3 Q07912 568 | hsa2880 V9HWN8 O75715 569 | hsa2050 P54760 Q541P7 Q96L35 570 | hsa2051 F8WCM8 O15197 J3KQU5 571 | hsa7201 P34981 572 | hsa2058 P07814 573 | hsa5096 P05166 574 | hsa5095 P05165 575 | hsa23035 Q6ZVD8 576 | hsa7172 P51580 A0A024QZW0 577 | hsa7173 Q6P534 Q502Y3 P07202 578 | hsa1138 Q6EWN4 P30532 579 | hsa1139 P36544 580 | hsa89822 Q96T54 581 | hsa9088 Q0IJ49 Q99640 582 | hsa90134 Q9NS40 583 | hsa8514 B2R776 Q13303 A0A0A6YYL8 A0A024R4E3 584 | hsa1131 A0A024R3S2 P20309 585 | hsa4881 P16066 586 | hsa1133 Q8IVW0 A0A024R9I2 P08912 587 | hsa1134 Q53SH4 P02708 588 | hsa1135 Q15822 589 | hsa1136 P32297 590 | hsa1137 P43681 Q4VAQ3 B4DK78 Q59FV0 591 | hsa3356 P28223 592 | hsa3357 P41595 593 | hsa3354 P28566 594 | hsa23620 O95665 595 | hsa3352 P28221 596 | hsa5320 P14555 A0A024RA96 597 | hsa3350 A8K5W4 Q5ZGX3 P08908 598 | hsa3351 P28222 X5D7I5 599 | hsa3055 J3KPD6 P08631 A8K4G3 600 | hsa9647 P49593 601 | hsa6529 P30531 A0A024R2G0 602 | hsa5328 A0A024QZM9 P00749 Q59GZ8 603 | hsa9312 Q92953 604 | hsa9641 Q14164 A0A075B7B4 605 | hsa64816 Q9HB55 606 | hsa1431 A0A024RB75 O75390 607 | hsa3765 Q92806 608 | hsa56922 Q96RQ3 Q68D27 E9PHF7 609 | hsa1432 B4E0K5 A0A024RD15 L7RSM2 Q16539 610 | hsa3939 P00338 V9HWB9 611 | hsa5241 P06401 612 | hsa28972 X6R2S6 Q9Y6A9 613 | hsa5979 Q9BTX6 P07949 A0A024R7T2 614 | hsa5146 P51160 615 | hsa670 Q86W56 Q49AI2 A0A024QZY0 Q86WA6 616 | hsa5972 P00797 617 | hsa3769 O60928 618 | hsa7421 F1D8P8 P11473 619 | hsa3716 P23458 620 | hsa3717 O60674 621 | hsa3768 Q14500 622 | hsa3712 A0A0A0MT83 P26440 623 | hsa63904 Q9H596 624 | hsa3710 A6H8K3 Q59ES2 Q14573 625 | hsa10327 P14550 V9HWI0 626 | hsa3718 P52333 A0A024R7M7 627 | hsa8529 P78329 628 | hsa2882 Q96SL4 629 | hsa7299 P14679 L8B082 630 | hsa23475 Q15274 V9HWJ5 631 | hsa2193 Q6IBR2 Q9Y285 632 | hsa10924 Q92484 633 | hsa4128 P21397 Q53YE7 Q49A63 634 | hsa4129 B7Z242 P27338 635 | hsa4282 P14174 I4AY87 636 | hsa285220 B3KS12 Q6UWM0 Q9UF33 637 | hsa6416 P45985 638 | hsa64600 Q9BZM2 639 | hsa51365 G5E9W0 Q53H76 640 | hsa55503 Q9H1D0 641 | hsa7054 P07101 P78428 642 | hsa6610 O60906 643 | hsa2066 Q15303 644 | hsa5140 A7E2E5 Q13370 645 | hsa9970 F1DAL4 Q14994 646 | hsa1504 647 | hsa1719 P00374 B0YJ76 B4DM58 648 | hsa5091 P11498 A0A024R5C5 649 | hsa54658 P22309 Q5DT03 650 | hsa54659 Q5DT01 P35503 651 | hsa5028 P47900 652 | hsa4145 P42679 F1T0G6 653 | hsa587 B3KSI3 O15382 654 | hsa586 A0A024RAV0 P54687 655 | hsa2876 P07203 Q7L4Q3 656 | hsa2877 P18283 657 | hsa2878 P22352 658 | hsa2879 P36969 Q6PI42 659 | hsa8288 P11678 660 | hsa5026 Q93086 661 | hsa5027 Q99572 662 | hsa5024 P56373 663 | hsa4907 Q6NZX3 P21589 664 | hsa4143 Q00266 665 | hsa81579 Q9BZM1 Q542Y6 666 | hsa200909 Q70Z44 F6WC43 667 | hsa5657 P24158 668 | hsa5651 P98073 669 | hsa93 Q13705 670 | hsa90 D3DPA4 Q04771 671 | hsa91 P36896 672 | hsa51181 Q7Z4W1 673 | hsa4758 Q5JQI0 Q99519 674 | hsa4051 A0A024R7I2 A0A024R7J8 Q08477 675 | hsa2232 A0A0A0MSZ4 A0A0A0MTN9 Q6GSK2 A0A0A0MT64 P22570 A0A0A0MTR6 676 | hsa2339 P49354 677 | hsa4058 A0A024R9Q5 P29376 678 | hsa2235 Q7KZA3 P22830 679 | hsa3752 A0A024R0C6 Q9UK17 680 | hsa2280 Q0VDC6 A0A087WTS4 P62942 681 | hsa6332 Q01118 682 | hsa231 A0A024R7A8 P15121 683 | hsa513 P30049 684 | hsa886 P32238 685 | hsa887 P32239 686 | hsa10161 A0A024RDT2 P43657 B3KVQ5 687 | hsa768 Q16790 688 | hsa767 P35219 689 | hsa766 P43166 690 | hsa765 P23280 B4DUH8 691 | hsa763 P35218 692 | hsa762 P22748 693 | hsa3945 Q5U077 P07195 694 | hsa760 P00918 V9HW21 695 | hsa63036 P08217 696 | hsa2135 Q9UBQ6 F5GZK1 Q8N8F1 697 | hsa1537 P08574 698 | hsa3755 Q9UIX4 Q86Y85 699 | hsa5053 A0A024RBG4 P00439 700 | hsa5052 Q06830 701 | hsa2098 P10768 702 | hsa5050 Q15102 A0A024R0L6 703 | hsa5729 Q13258 704 | hsa10858 Q9Y6A2 705 | hsa25 Q59FK4 A0A024R8E2 P00519 706 | hsa6646 P35610 707 | hsa28 P16442 A0A089QDC1 708 | hsa1610 A0A024RBI1 P14920 709 | hsa26251 Q9UJ96 710 | hsa9420 Q05C57 O75881 711 | hsa2242 A0A024RC92 P07332 712 | hsa2241 Q05DA5 P16591 W0S0X4 713 | hsa2049 P54753 714 | hsa3033 Q16836 E9PF18 715 | hsa240 P09917 716 | hsa6714 P12931 717 | hsa242 O75342 718 | hsa23097 I6W807 Q9BWU1 B4DUB1 719 | hsa246 P16050 720 | hsa247 O15296 721 | hsa80824 Q9BY84 A0A024RAR2 722 | hsa91039 Q86TI2 723 | hsa7378 B4DND0 Q86Y75 Q16831 724 | hsa5834 P11216 725 | hsa5837 P11217 726 | hsa5836 P06737 727 | hsa5831 A0A024R8U9 P32322 E2QRB3 Q8TBX0 J3QL32 728 | hsa6336 Q9Y5Y9 729 | hsa7371 A0A024R912 Q9BZX2 730 | hsa7372 P11172 A8K5J1 731 | hsa4837 Q6FH49 P40261 732 | hsa4835 P16083 B3KPX6 Q5TD07 733 | hsa645 V9HWI1 P30043 734 | hsa2147 P00734 735 | hsa3291 P80365 736 | hsa57176 B4E0K6 Q5ST30 B4DG77 737 | hsa1132 P08173 738 | hsa2065 P21860 739 | hsa2064 F5H1T4 J3QLU9 P04626 X5DNK3 740 | hsa1141 P17787 Q5SXY3 741 | hsa1140 P11230 742 | hsa3758 A8K432 P48048 A0A024R3K6 743 | hsa3759 P63252 744 | hsa1145 Q04844 745 | hsa1144 Q07001 746 | hsa3615 P12268 747 | hsa3614 P20839 B3KRZ3 748 | hsa129807 Q3KR05 Q8WWR8 B3KR54 749 | hsa3753 A7LFK4 C7S316 P15382 Q6FHJ6 A7LFK2 750 | hsa3750 Q9NSA2 751 | hsa3751 A4D0V9 Q75LS7 Q9NZV8 752 | hsa3756 O95259 753 | hsa3757 A0A090N7W1 A0A090N8Q0 Q12809 A0A090N7X5 Q15BH2 754 | hsa3754 Q9H3M0 755 | hsa1267 A0A024R1T5 P09543 756 | hsa5475 O14829 A0A024RBZ9 B7Z6R7 757 | hsa5476 P10619 X6R8A1 B4E324 X6R5C5 758 | hsa3002 Q67BC3 P10144 759 | hsa5470 O14830 760 | hsa3001 P12544 761 | hsa7015 O14746 762 | hsa6261 P21817 763 | hsa5479 P23284 764 | hsa7010 Q59HG2 Q02763 765 | hsa10825 A0A024R5N6 Q9UQ49 766 | hsa1545 Q16678 Q53TK1 767 | hsa2180 E7EPM6 B7Z3Z9 P33121 B4E0R0 A8K9T3 768 | hsa54878 Q6V1X1 J3KPT0 A0A024R5Z6 769 | hsa51004 Q9Y2Z9 B7Z357 770 | hsa1543 A0N0X8 P04798 771 | hsa124 P07327 772 | hsa22978 P49902 A8K6K2 773 | hsa6262 Q92736 774 | hsa3363 P34969 775 | hsa1548 P11509 776 | hsa3361 P47898 A4D2N2 777 | hsa3360 Q13639 778 | hsa41 A8K1U5 P78348 779 | hsa4593 A0A087WSY1 O15146 780 | hsa126 P00326 781 | hsa84152 B3KVQ9 A0A024R1R3 Q9UD71 782 | hsa4594 A0A024RD82 P22033 B2R6K1 783 | hsa5625 O43272 784 | hsa5321 P47712 785 | hsa9934 Q15391 A5JUU3 786 | hsa56413 B4E292 Q9NPC1 787 | hsa6300 Q6N076 P53778 B7Z274 788 | hsa55902 Q96FY7 Q6DKJ3 Q9NR19 Q4G0E8 789 | hsa2155 P08709 B4DPM2 F5H8B0 790 | hsa1312 P21964 791 | hsa8398 O60733 A8K460 792 | hsa217 P05091 793 | hsa1549 F8W816 P20853 794 | hsa2936 V9HW90 P00390 795 | hsa3362 P50406 796 | hsa4233 P08581 A0A024R728 B4DLF5 A0A024R759 797 | hsa8555 O60729 798 | hsa9283 O60883 799 | hsa8556 Q9UNH5 Q59EF4 800 | hsa23436 P08861 801 | hsa8558 B7Z537 Q15131 F8W872 802 | hsa5516 P62714 803 | hsa1969 P29317 A0A024QZA8 804 | hsa8001 O75311 Q9UPF3 805 | hsa5515 B3KUN1 P67775 806 | hsa7083 P04183 807 | hsa7084 Q8IZR3 A4IF54 B4DIW4 B4E0Z4 O00142 A5PLM0 808 | hsa338442 Q8TDS4 809 | hsa51251 A0A090N7U2 Q9H0P0 A0A024RA81 810 | hsa9311 A0A090N7X8 A0A090N8Q1 A0A090N8Z6 Q9UHC3 811 | hsa54579 P35504 Q5DSZ9 812 | hsa54578 P19224 Q5DT01 Q5DSZ8 813 | hsa5025 Q99571 814 | hsa27133 Q8NCM2 815 | hsa84706 Q8TD30 A0A024R6R2 816 | hsa51095 Q96Q11 A0A024R2H7 817 | hsa55775 A0A024R6L5 Q9NUW8 B3KN41 818 | hsa3359 B4E398 P46098 819 | hsa54575 Q5DT02 Q9HAW8 820 | hsa54577 Q5DSZ7 Q9HAW7 821 | hsa54576 Q5DSZ6 Q9HAW9 822 | hsa64499 P20231 823 | hsa5158 B4DHV7 P35913 824 | hsa5159 P09619 Q59F04 825 | hsa5033 Q5VSQ6 P13674 826 | hsa10295 O14874 A0A024QZA9 827 | hsa5152 O76083 828 | hsa5150 Q13946 829 | hsa5151 O60658 A0A024RC53 830 | hsa5156 P16234 831 | hsa29968 A0A024R222 Q9Y617 A0A024R280 832 | hsa5606 Q6FI23 P46734 833 | hsa5607 Q13163 A0A024R5Y2 A0A024R5X5 834 | hsa5604 A4QPA9 Q02750 835 | hsa1436 P07333 A2VDG3 836 | hsa5602 A8MWW6 Q499Y8 P53779 837 | hsa5603 O15264 A0A024RD04 838 | hsa5600 Q15759 839 | hsa5601 P45984 840 | hsa761 V9HWA3 P07451 841 | hsa6609 P17405 Q59EN6 842 | hsa83795 Q96T55 843 | hsa5608 P52564 A0A024R8K3 A8K3Y2 844 | hsa2806 P00505 845 | hsa11266 Q9UNI6 846 | hsa834 A8K257 P29466 A8K249 847 | hsa6768 Q9Y5Y6 848 | hsa51172 Q9UK23 849 | hsa2563 O14764 A8K496 850 | hsa2560 X5DNL6 P18505 851 | hsa2561 P47870 852 | hsa2566 P18507 853 | hsa5562 Q13131 854 | hsa2564 P78334 855 | hsa27345 Q86W47 856 | hsa3767 Q14654 B2RC52 857 | hsa30833 V9HWF3 J3KRC4 Q8TCD5 858 | hsa51166 Q8N5Z0 Q4W5N8 859 | hsa9150 Q9Y5B0 A0A0A0MR03 860 | hsa9254 Q9NY47 C9JE82 861 | hsa5327 P00750 862 | hsa613 P11274 863 | hsa759 P00915 V9HWE3 864 | hsa3764 Q15842 A0A024RAV6 865 | hsa5126 P16519 866 | hsa5122 P29120 867 | hsa5478 P62937 V9HWF5 868 | hsa5594 Q499G7 P28482 Q1HBJ4 869 | hsa130399 Q8NER5 870 | hsa1645 Q04828 871 | hsa51302 B7Z786 Q9NYL5 A0A087WTD2 872 | hsa1728 B4DLR8 P15559 873 | hsa2898 Q13002 Q8IY40 F8WEZ8 A8K0H7 874 | hsa2899 Q13003 875 | hsa1725 A0A024R7D0 P49366 A0A024R7E9 876 | hsa2897 P39086 E7EPZ0 E7EPY9 E7ENK3 E9PD61 877 | hsa55359 Q6J9G0 878 | hsa2892 Q5XKG2 Q17R51 P42263 879 | hsa2893 P48058 Q1WWK6 880 | hsa1723 Q02127 881 | hsa2891 P42262 882 | hsa10800 Q38Q91 Q9Y271 Q38Q88 883 | hsa3988 P38571 A0A0A0MT32 884 | hsa5051 Q99487 885 | hsa2101 P11474 Q569H8 886 | hsa683 Q10588 887 | hsa2104 P62508 B7Z5E9 C0SQ93 F1D8R6 F1D8R5 888 | hsa5595 L7RXH5 P27361 Q9BWJ1 889 | hsa686 P43251 890 | hsa64087 Q9HCC0 891 | hsa2638 P02774 892 | hsa2639 Q92947 A0A024R7F9 893 | hsa2322 P36888 894 | hsa132160 Q96MI6 B7XGB9 895 | hsa5332 Q15147 896 | hsa5333 A8K8F9 P51178 897 | hsa5330 Q9BVT6 Q00722 Q59F77 898 | hsa5331 Q01970 899 | hsa5336 P16885 900 | hsa5337 Q13393 Q59EA4 901 | hsa5335 Q9UFY1 P19174 Q4LE43 902 | hsa55811 Q96PN6 903 | hsa4200 P23368 904 | hsa5338 O14939 905 | hsa57192 Q9GZU1 906 | hsa7301 Q06418 907 | hsa1588 A0A024R5S8 A8K6W3 Q8TCA4 P11511 Q8IYG4 Q05CU4 908 | hsa6340 A5X2V1 P51170 909 | hsa2326 A0A024R934 Q01740 B2RCG5 910 | hsa1581 P22680 911 | hsa1580 P13584 912 | hsa1583 P05108 913 | hsa1582 Q9UNU6 914 | hsa1585 P19099 915 | hsa1584 Q4VAR0 Q8TDD0 P15538 916 | hsa1586 P05093 Q1HB44 917 | hsa2742 P23416 918 | hsa2743 P48167 919 | hsa2741 P23415 Q14C71 920 | hsa2746 P00367 E9KL48 921 | hsa1033 F5H232 Q16667 922 | hsa53938 Q9H2H8 A0A024R3V4 923 | hsa5860 P09417 924 | hsa25824 P30044 925 | hsa3763 P48051 926 | hsa3762 P48544 927 | hsa3761 Q58F07 A0A024R1L8 P48050 928 | hsa3760 D2XBF0 P48549 D2X9V0 929 | hsa1215 P23946 Q4FEB3 930 | hsa3766 P78508 931 | hsa8513 P07098 932 | hsa79799 Q6UWM9 933 | hsa3480 C9J5X1 P08069 934 | hsa1429 Q08257 935 | hsa339221 Q6UWV6 936 | hsa7046 Q5T7S2 P36897 937 | hsa94 P37023 938 | hsa1544 P05177 939 | hsa3645 P14616 940 | hsa9023 O95992 941 | hsa3643 P06213 942 | hsa5322 P39877 943 | hsa22843 Q8WY54 A7E2X1 944 | hsa5340 P00747 Q5TEH5 945 | hsa3035 B4DDD8 B4E1C5 B3KWE1 P12081 946 | hsa3034 P42357 947 | hsa5422 P09884 A6NMQ1 948 | hsa5423 B7Z1W5 P06746 949 | hsa6337 P37088 950 | hsa4281 A0A087X255 A0A024RBV4 A0A087X0X0 O15344 951 | hsa6335 Q15858 952 | hsa6334 Q9UQD0 953 | hsa9945 O94808 954 | hsa390956 955 | hsa6331 Q14524 K4DIA1 E9PHB6 Q86V90 E9PG18 H9KVD2 956 | hsa6330 Q8IWT1 B0YJ93 957 | hsa660 P51813 958 | hsa6339 A6NNF7 P51172 959 | hsa6338 B2R812 P51168 960 | hsa133121 Q6UWR7 961 | hsa79001 Q9BQB6 962 | hsa2846 Q99677 963 | hsa5523 Q06190 964 | hsa11221 Q9Y6W6 965 | hsa5481 E5KN55 Q08752 966 | hsa6725 Q9H3Y6 967 | hsa3612 P29218 A0A024R830 968 | hsa55799 Q8IZS8 969 | hsa4919 Q01973 970 | hsa11343 B2ZGL7 Q99685 E7EWX8 971 | hsa5646 Q7Z5F4 P35030 972 | hsa501 P49419 973 | hsa5645 Q5NV56 P07478 A6XMV8 A6XMV9 974 | hsa2321 L7RSL3 P17948 975 | hsa8940 A8K4N2 A0A024R1C2 O95985 976 | hsa8192 Q16740 977 | hsa2324 P35916 978 | hsa1812 P21728 979 | hsa4067 Q6NUK7 P07948 A8K379 980 | hsa2328 Q53FW5 A0A024R8Z4 P31513 981 | hsa4017 Q9Y4K0 982 | hsa279 P04746 983 | hsa8399 O15496 984 | hsa285242 A5X5Y0 985 | hsa2159 Q5JVE8 Q5JVE7 P00742 986 | hsa55312 Q969G6 B2RDZ2 987 | hsa84532 Q9NUB1 Q1RMZ4 988 | hsa7881 F8W6W4 Q14722 B7Z8E5 989 | hsa3292 P14061 990 | hsa6897 P26639 991 | -------------------------------------------------------------------------------- /datasets/README: -------------------------------------------------------------------------------- 1 | These datasets are collected from http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/. 2 | -------------------------------------------------------------------------------- /datasets/nr_admat_dgc.txt: -------------------------------------------------------------------------------- 1 | D00040 D00066 D00067 D00075 D00088 D00094 D00105 D00129 D00143 D00163 D00182 D00187 D00188 D00211 D00246 D00279 D00299 D00312 D00316 D00327 D00348 D00443 D00462 D00506 D00554 D00565 D00577 D00585 D00586 D00596 D00627 D00690 D00730 D00898 D00930 D00950 D00951 D00954 D00956 D00961 D00962 D00965 D01115 D01132 D01161 D01217 D01294 D01387 D01441 D01689 D02217 D02367 D04066 D05341 2 | hsa190 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 | hsa2099 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 4 | hsa2100 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 5 | hsa2101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 | hsa2103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 | hsa2104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 | hsa2908 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 9 | hsa3174 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 | hsa367 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 11 | hsa4306 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 12 | hsa5241 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 13 | hsa5465 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 | hsa5468 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 | hsa5914 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 16 | hsa5915 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 17 | hsa5916 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 18 | hsa6095 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 19 | hsa6096 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 20 | hsa6097 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 | hsa6256 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 22 | hsa6257 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 23 | hsa6258 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 24 | hsa7421 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 | hsa8856 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 | hsa9970 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 | hsa9971 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 28 | -------------------------------------------------------------------------------- /datasets/nr_simmat_dc.txt: -------------------------------------------------------------------------------- 1 | D00040 D00066 D00067 D00075 D00088 D00094 D00105 D00129 D00143 D00163 D00182 D00187 D00188 D00211 D00246 D00279 D00299 D00312 D00316 D00327 D00348 D00443 D00462 D00506 D00554 D00565 D00577 D00585 D00586 D00596 D00627 D00690 D00730 D00898 D00930 D00950 D00951 D00954 D00956 D00961 D00962 D00965 D01115 D01132 D01161 D01217 D01294 D01387 D01441 D01689 D02217 D02367 D04066 D05341 2 | D00040 1 0.545455 0.297297 0.53125 0.459459 0.190476 0.333333 0.526316 0.7 0.6 0.428571 0.540541 0.647059 0.208333 0.372093 0.073171 0.5 0.232558 0.125 0.444444 0.190476 0.425 0.470588 0.046512 0.282051 0.104167 0.066667 0.363636 0.068182 0.070175 0.080645 0.26 0.076923 0.066667 0.414634 0.416667 0.473684 0.416667 0.414634 0.055556 0.076923 0.041667 0.348837 0.104167 0.277778 0.5 0.4 0.306122 0.129032 0.363636 0.125 0.378378 0.266667 0.095238 3 | D00066 0.545455 1 0.387097 0.833333 0.689655 0.184211 0.387097 0.358974 0.769231 0.457143 0.607143 0.368421 0.378378 0.188406 0.459459 0.083333 0.333333 0.333333 0.065217 0.62069 0.184211 0.677419 0.5 0.111111 0.323529 0.090909 0.04878 0.447368 0.105263 0.076923 0.050847 0.380952 0.027778 0.04878 0.358974 0.586207 0.7 0.586207 0.514286 0.061224 0.031746 0.097561 0.472222 0.116279 0.207547 0.84 0.5 0.404762 0.140351 0.447368 0.115385 0.4375 0.3 0.025 4 | D00067 0.297297 0.387097 1 0.464286 0.352941 0.105263 0.818182 0.25 0.34375 0.297297 0.448276 0.25641 0.263158 0.161765 0.275 0.2 0.25641 0.730769 0.121951 0.375 0.105263 0.289474 0.4 0.233333 0.75 0.153846 0.212121 0.405405 0.21875 0.204545 0.113208 0.309524 0.214286 0.212121 0.25 0.433333 0.297297 0.433333 0.470588 0.166667 0.107143 0.3125 0.25 0.25 0.386364 0.34375 0.297297 0.244444 0.215686 0.3 0.25 0.30303 0.53125 0.027027 5 | D00075 0.53125 0.833333 0.464286 1 0.678571 0.162162 0.464286 0.342105 0.62963 0.441176 0.72 0.351351 0.361111 0.19403 0.444444 0.057143 0.315789 0.352941 0.044444 0.666667 0.162162 0.5625 0.535714 0.117647 0.387097 0.045455 0.078947 0.472222 0.052632 0.08 0.034483 0.365854 0.029412 0.078947 0.342105 0.692308 0.580645 0.692308 0.545455 0.041667 0.05 0.102564 0.457143 0.121951 0.24 0.692308 0.484848 0.390244 0.105263 0.472222 0.098039 0.517241 0.351351 0.026316 6 | D00088 0.459459 0.689655 0.352941 0.678571 1 0.142857 0.352941 0.333333 0.580645 0.421053 0.6 0.27907 0.317073 0.231884 0.628571 0.076923 0.309524 0.275 0.061224 0.724138 0.142857 0.527778 0.454545 0.102564 0.371429 0.085106 0.095238 0.487179 0.097561 0.092593 0.048387 0.487805 0.052632 0.095238 0.333333 0.580645 0.588235 0.580645 0.435897 0.078431 0.0625 0.090909 0.473684 0.108696 0.218182 0.580645 0.421053 0.47619 0.133333 0.526316 0.150943 0.484848 0.27907 0.047619 7 | D00094 0.190476 0.184211 0.105263 0.162162 0.142857 1 0.105263 0.238095 0.184211 0.219512 0.128205 0.342105 0.25 0.125 0.152174 0.085714 0.342105 0.093023 0.371429 0.179487 1 0.159091 0.157895 0.054054 0.1 0.093023 0.076923 0.173913 0.051282 0.1 0.109091 0.14 0.028571 0.076923 0.333333 0.125 0.190476 0.125 0.106383 0.02 0.103448 0.073171 0.155556 0.146341 0.067797 0.184211 0.162791 0.137255 0.084746 0.148936 0.055556 0.125 0.085106 0.111111 8 | D00105 0.333333 0.387097 0.818182 0.464286 0.352941 0.105263 1 0.282051 0.387097 0.333333 0.4 0.289474 0.297297 0.161765 0.275 0.2 0.289474 0.607143 0.121951 0.333333 0.105263 0.25641 0.4 0.15625 0.826087 0.153846 0.25 0.405405 0.21875 0.152174 0.113208 0.27907 0.214286 0.25 0.282051 0.34375 0.263158 0.34375 0.470588 0.195122 0.127273 0.235294 0.219512 0.25 0.452381 0.34375 0.297297 0.217391 0.215686 0.268293 0.27907 0.34375 0.633333 0.027027 9 | D00129 0.526316 0.358974 0.25 0.342105 0.333333 0.238095 0.282051 1 0.394737 0.487179 0.3 0.638889 0.8125 0.202703 0.297872 0.095238 0.512821 0.195652 0.142857 0.35 0.238095 0.282609 0.333333 0.044444 0.268293 0.1 0.086957 0.319149 0.065217 0.105263 0.078125 0.226415 0.073171 0.086957 0.714286 0.292683 0.318182 0.292683 0.304348 0.072727 0.090909 0.061224 0.276596 0.1 0.267857 0.325 0.288889 0.294118 0.125 0.265306 0.140351 0.325 0.255319 0.090909 10 | D00143 0.7 0.769231 0.34375 0.62963 0.580645 0.184211 0.387097 0.394737 1 0.545455 0.5 0.444444 0.457143 0.188406 0.384615 0.083333 0.405405 0.263158 0.065217 0.516129 0.184211 0.575758 0.551724 0.052632 0.323529 0.090909 0.075 0.375 0.105263 0.037037 0.050847 0.318182 0.027778 0.075 0.394737 0.483871 0.545455 0.483871 0.394737 0.083333 0.048387 0.046512 0.394737 0.116279 0.230769 0.703704 0.457143 0.372093 0.140351 0.375 0.137255 0.4375 0.268293 0.025 11 | D00163 0.6 0.457143 0.297297 0.441176 0.421053 0.219512 0.333333 0.487179 0.545455 1 0.351351 0.390244 0.473684 0.191781 0.311111 0.073171 0.357143 0.232558 0.08 0.405405 0.219512 0.425 0.515152 0.046512 0.315789 0.081633 0.090909 0.276596 0.044444 0.109091 0.116667 0.26 0.076923 0.090909 0.45 0.342105 0.365854 0.342105 0.348837 0.055556 0.111111 0.041667 0.348837 0.104167 0.277778 0.416667 0.365854 0.306122 0.129032 0.304348 0.145455 0.342105 0.295455 0.15 12 | D00182 0.428571 0.607143 0.448276 0.72 0.6 0.128205 0.4 0.3 0.5 0.351351 1 0.307692 0.315789 0.173913 0.358974 0.027027 0.307692 0.342857 0.021277 0.586207 0.128205 0.5 0.466667 0.114286 0.517241 0.021739 0.05 0.5 0.025 0.078431 0.016667 0.295455 0.028571 0.05 0.3 0.875 0.515152 0.875 0.529412 0.0625 0.032258 0.1 0.405405 0.119048 0.211538 0.5 0.612903 0.318182 0.084746 0.384615 0.096154 0.666667 0.307692 0.025641 13 | D00187 0.540541 0.368421 0.25641 0.351351 0.27907 0.342105 0.289474 0.638889 0.444444 0.390244 0.307692 1 0.78125 0.173333 0.25 0.071429 0.757576 0.2 0.195652 0.358974 0.342105 0.26087 0.342105 0.045455 0.243902 0.102041 0.065217 0.326087 0.066667 0.068966 0.046154 0.207547 0.02381 0.065217 0.735294 0.3 0.357143 0.3 0.255319 0.035714 0.044118 0.040816 0.282609 0.102041 0.166667 0.333333 0.295455 0.27451 0.109375 0.244898 0.084746 0.333333 0.208333 0.021739 14 | D00188 0.647059 0.378378 0.263158 0.361111 0.317073 0.25 0.297297 0.8125 0.457143 0.473684 0.315789 0.78125 1 0.175676 0.311111 0.073171 0.583333 0.204545 0.148936 0.368421 0.25 0.295455 0.351351 0.046512 0.25 0.104167 0.066667 0.333333 0.068182 0.109091 0.080645 0.235294 0.076923 0.066667 0.567568 0.307692 0.333333 0.307692 0.318182 0.055556 0.076923 0.041667 0.288889 0.104167 0.232143 0.342105 0.302326 0.306122 0.129032 0.276596 0.125 0.342105 0.266667 0.095238 15 | D00211 0.208333 0.188406 0.161765 0.19403 0.231884 0.125 0.161765 0.219178 0.188406 0.191781 0.173913 0.173333 0.175676 1 0.216216 0.119403 0.205479 0.135135 0.197183 0.220588 0.125 0.189189 0.140845 0.101449 0.157143 0.150685 0.112676 0.197368 0.068493 0.108434 0.101124 0.220779 0.073529 0.112676 0.202703 0.171429 0.26087 0.171429 0.202703 0.08642 0.074468 0.125 0.202703 0.166667 0.136364 0.205882 0.191781 0.266667 0.16092 0.22973 0.189873 0.15493 0.157895 0.013158 16 | D00246 0.372093 0.459459 0.275 0.444444 0.628571 0.152174 0.275 0.297872 0.384615 0.311111 0.358974 0.25 0.311111 0.216216 1 0.093023 0.276596 0.217391 0.075472 0.486486 0.152174 0.395349 0.325 0.090909 0.261905 0.098039 0.108696 0.340426 0.086957 0.103448 0.09375 0.571429 0.071429 0.085106 0.297872 0.35 0.439024 0.35 0.326087 0.090909 0.089552 0.081633 0.355556 0.12 0.2 0.421053 0.311111 0.634146 0.140625 0.575 0.157895 0.285714 0.276596 0.088889 17 | D00279 0.073171 0.083333 0.2 0.057143 0.076923 0.085714 0.2 0.095238 0.083333 0.073171 0.027027 0.071429 0.073171 0.119403 0.093023 1 0.071429 0.205882 0.354839 0.111111 0.085714 0.071429 0.085714 0.269231 0.1875 0.518519 0.333333 0.230769 0.25 0.225 0.222222 0.214286 0.5 0.285714 0.095238 0.054054 0.157895 0.054054 0.243243 0.25 0.208333 0.225806 0.121951 0.413793 0.14 0.083333 0.128205 0.155556 0.183673 0.2 0.186047 0.054054 0.25 0.0625 18 | D00299 0.425 0.3 0.25641 0.351351 0.309524 0.342105 0.289474 0.512821 0.405405 0.390244 0.307692 0.757576 0.583333 0.205479 0.276596 0.071429 1 0.2 0.222222 0.358974 0.342105 0.288889 0.342105 0.045455 0.243902 0.102041 0.065217 0.355556 0.066667 0.068966 0.046154 0.230769 0.02381 0.065217 0.735294 0.3 0.357143 0.3 0.255319 0.035714 0.044118 0.040816 0.282609 0.102041 0.186441 0.333333 0.325581 0.27451 0.109375 0.244898 0.084746 0.333333 0.208333 0.021739 19 | D00312 0.232558 0.333333 0.730769 0.352941 0.275 0.093023 0.607143 0.195652 0.263158 0.232558 0.342857 0.2 0.204545 0.135135 0.217391 0.205882 0.2 1 0.133333 0.289474 0.093023 0.255814 0.305556 0.2 0.566667 0.162791 0.153846 0.357143 0.222222 0.208333 0.103448 0.25 0.181818 0.153846 0.195652 0.333333 0.261905 0.333333 0.410256 0.227273 0.116667 0.305556 0.222222 0.219512 0.375 0.297297 0.261905 0.196078 0.264151 0.23913 0.2 0.230769 0.459459 0.02381 20 | D00316 0.125 0.065217 0.121951 0.044444 0.061224 0.371429 0.121951 0.142857 0.065217 0.08 0.021277 0.195652 0.148936 0.197183 0.075472 0.354839 0.222222 0.133333 1 0.06383 0.371429 0.057692 0.066667 0.102564 0.116279 0.275 0.179487 0.137255 0.153846 0.156863 0.160714 0.12963 0.212121 0.15 0.191489 0.042553 0.148936 0.042553 0.12 0.122449 0.133333 0.142857 0.098039 0.275 0.098361 0.065217 0.102041 0.107143 0.133333 0.137255 0.12963 0.042553 0.170213 0.047619 21 | D00327 0.444444 0.62069 0.375 0.666667 0.724138 0.179487 0.333333 0.35 0.516129 0.405405 0.586207 0.358974 0.368421 0.220588 0.486486 0.111111 0.358974 0.289474 0.06383 1 0.179487 0.472222 0.483871 0.108108 0.352941 0.088889 0.1 0.513514 0.075 0.075472 0.05 0.475 0.027027 0.1 0.35 0.566667 0.485714 0.566667 0.384615 0.06 0.064516 0.121951 0.542857 0.113636 0.203704 0.516129 0.405405 0.463415 0.118644 0.513514 0.134615 0.46875 0.292683 0.02439 22 | D00348 0.190476 0.184211 0.105263 0.162162 0.142857 1 0.105263 0.238095 0.184211 0.219512 0.128205 0.342105 0.25 0.125 0.152174 0.085714 0.342105 0.093023 0.371429 0.179487 1 0.159091 0.157895 0.054054 0.1 0.093023 0.076923 0.173913 0.051282 0.1 0.109091 0.14 0.028571 0.076923 0.333333 0.125 0.190476 0.125 0.106383 0.02 0.103448 0.073171 0.155556 0.146341 0.067797 0.184211 0.162791 0.137255 0.084746 0.148936 0.055556 0.125 0.085106 0.111111 23 | D00443 0.425 0.677419 0.289474 0.5625 0.527778 0.159091 0.25641 0.282609 0.575758 0.425 0.5 0.26087 0.266667 0.189189 0.395349 0.071429 0.288889 0.255814 0.057692 0.472222 0.159091 1 0.5 0.095238 0.275 0.08 0.042553 0.386364 0.090909 0.068966 0.046154 0.333333 0.02381 0.042553 0.282609 0.485714 0.540541 0.485714 0.372093 0.054545 0.028986 0.085106 0.685714 0.102041 0.186441 0.625 0.425 0.354167 0.126984 0.386364 0.103448 0.368421 0.208333 0.021739 24 | D00462 0.470588 0.5 0.4 0.535714 0.454545 0.157895 0.4 0.333333 0.551724 0.515152 0.466667 0.342105 0.351351 0.140845 0.325 0.085714 0.342105 0.305556 0.066667 0.483871 0.157895 0.5 1 0.054054 0.419355 0.068182 0.076923 0.35 0.051282 0.037736 0.051724 0.266667 0.028571 0.076923 0.368421 0.451613 0.388889 0.451613 0.3 0.040816 0.04918 0.047619 0.444444 0.119048 0.211538 0.40625 0.388889 0.348837 0.122807 0.35 0.096154 0.451613 0.307692 0.025641 25 | D00506 0.046512 0.111111 0.233333 0.117647 0.102564 0.054054 0.15625 0.044444 0.052632 0.046512 0.114286 0.045455 0.046512 0.101449 0.090909 0.269231 0.045455 0.2 0.102564 0.108108 0.054054 0.095238 0.054054 1 0.147059 0.166667 0.233333 0.195122 0.16129 0.282051 0.142857 0.155556 0.291667 0.193548 0.044444 0.142857 0.097561 0.142857 0.236842 0.15 0.156863 0.344828 0.093023 0.166667 0.09434 0.111111 0.046512 0.081633 0.113208 0.088889 0.130435 0.081081 0.121951 0.060606 26 | D00554 0.282051 0.323529 0.75 0.387097 0.371429 0.1 0.826087 0.268293 0.323529 0.315789 0.517241 0.243902 0.25 0.157143 0.261905 0.1875 0.243902 0.566667 0.116279 0.352941 0.1 0.275 0.419355 0.147059 1 0.146341 0.235294 0.459459 0.205882 0.145833 0.109091 0.295455 0.2 0.235294 0.268293 0.451613 0.282051 0.451613 0.405405 0.186047 0.122807 0.222222 0.238095 0.27027 0.4 0.285714 0.388889 0.208333 0.207547 0.285714 0.266667 0.40625 0.545455 0.025641 27 | D00565 0.104167 0.090909 0.153846 0.045455 0.085106 0.093023 0.153846 0.1 0.090909 0.081633 0.021739 0.102041 0.104167 0.150685 0.098039 0.518519 0.102041 0.162791 0.275 0.088889 0.093023 0.08 0.068182 0.166667 0.146341 1 0.451613 0.212766 0.294118 0.26087 0.254902 0.22449 0.3 0.451613 0.1 0.021277 0.177778 0.021277 0.195652 0.384615 0.288462 0.205128 0.1 0.428571 0.137931 0.090909 0.104167 0.150943 0.288462 0.14 0.304348 0.021277 0.2 0.02381 28 | D00577 0.066667 0.04878 0.212121 0.078947 0.095238 0.076923 0.25 0.086957 0.075 0.090909 0.05 0.065217 0.066667 0.112676 0.108696 0.333333 0.065217 0.153846 0.179487 0.1 0.076923 0.042553 0.076923 0.233333 0.235294 0.451613 1 0.209302 0.21875 0.292683 0.282609 0.122449 0.36 0.818182 0.086957 0.075 0.043478 0.075 0.162791 0.361111 0.347826 0.2 0.041667 0.40625 0.173077 0.04878 0.043478 0.056604 0.265306 0.083333 0.341463 0.075 0.195122 0.085714 29 | D00585 0.363636 0.447368 0.405405 0.472222 0.487179 0.173913 0.405405 0.319149 0.375 0.276596 0.5 0.326087 0.333333 0.197368 0.340426 0.230769 0.355556 0.357143 0.137255 0.513514 0.173913 0.386364 0.35 0.195122 0.459459 0.212766 0.209302 1 0.159091 0.181818 0.145161 0.395833 0.179487 0.209302 0.291667 0.486486 0.428571 0.486486 0.47619 0.173077 0.138462 0.2 0.409091 0.23913 0.237288 0.375 0.395349 0.333333 0.213115 0.361702 0.196429 0.410256 0.326087 0.020408 30 | D00586 0.068182 0.105263 0.21875 0.052632 0.097561 0.051282 0.21875 0.065217 0.105263 0.044444 0.025 0.066667 0.068182 0.068493 0.086957 0.25 0.066667 0.222222 0.153846 0.075 0.051282 0.090909 0.051282 0.16129 0.205882 0.294118 0.21875 0.159091 1 0.130435 0.137255 0.2 0.222222 0.21875 0.065217 0.02439 0.093023 0.02439 0.113636 0.411765 0.150943 0.576923 0.042553 0.222222 0.2 0.105263 0.044444 0.1 0.22 0.0625 0.2 0.02439 0.230769 0.027778 31 | D00596 0.070175 0.076923 0.204545 0.08 0.092593 0.1 0.152174 0.105263 0.037037 0.109091 0.078431 0.068966 0.109091 0.108434 0.103448 0.225 0.068966 0.208333 0.156863 0.075472 0.1 0.068966 0.037736 0.282051 0.145833 0.26087 0.292683 0.181818 0.130435 1 0.333333 0.152542 0.236842 0.292683 0.067797 0.076923 0.070175 0.076923 0.188679 0.24 0.388889 0.222222 0.067797 0.26087 0.138462 0.076923 0.033898 0.078125 0.171875 0.083333 0.259259 0.037037 0.169811 0.159091 32 | D00627 0.080645 0.050847 0.113208 0.034483 0.048387 0.109091 0.113208 0.078125 0.050847 0.116667 0.016667 0.046154 0.080645 0.101124 0.09375 0.222222 0.046154 0.103448 0.160714 0.05 0.109091 0.046154 0.051724 0.142857 0.109091 0.254902 0.282609 0.145161 0.137255 0.333333 1 0.121212 0.204545 0.255319 0.045455 0.033333 0.046875 0.033333 0.112903 0.236364 0.285714 0.12963 0.045455 0.230769 0.126761 0.050847 0.046875 0.056338 0.372881 0.075758 0.298246 0.033333 0.152542 0.14 33 | D00690 0.26 0.380952 0.309524 0.365854 0.487805 0.14 0.27907 0.226415 0.318182 0.26 0.295455 0.207547 0.235294 0.220779 0.571429 0.214286 0.230769 0.25 0.12963 0.475 0.14 0.333333 0.266667 0.155556 0.295455 0.22449 0.122449 0.395833 0.2 0.152542 0.121212 1 0.195122 0.122449 0.226415 0.288889 0.465116 0.288889 0.413043 0.163636 0.132353 0.1875 0.413043 0.22449 0.225806 0.318182 0.3125 0.613636 0.184615 0.595238 0.166667 0.234043 0.361702 0.039216 34 | D00730 0.076923 0.027778 0.214286 0.029412 0.052632 0.028571 0.214286 0.073171 0.027778 0.076923 0.028571 0.02381 0.076923 0.073529 0.071429 0.5 0.02381 0.181818 0.212121 0.027027 0.028571 0.02381 0.028571 0.291667 0.2 0.3 0.36 0.179487 0.222222 0.236842 0.204545 0.195122 1 0.259259 0.023256 0.057143 0.105263 0.057143 0.257143 0.228571 0.191489 0.2 0.1 0.344828 0.170213 0.027778 0.105263 0.111111 0.142857 0.15 0.195122 0.057143 0.264706 0.142857 35 | D00898 0.066667 0.04878 0.212121 0.078947 0.095238 0.076923 0.25 0.086957 0.075 0.090909 0.05 0.065217 0.066667 0.112676 0.085106 0.285714 0.065217 0.153846 0.15 0.1 0.076923 0.042553 0.076923 0.193548 0.235294 0.451613 0.818182 0.209302 0.21875 0.292683 0.255319 0.122449 0.259259 1 0.086957 0.04878 0.043478 0.04878 0.136364 0.324324 0.347826 0.2 0.041667 0.363636 0.150943 0.04878 0.043478 0.056604 0.265306 0.061224 0.309524 0.04878 0.166667 0.027027 36 | D00930 0.45 0.358974 0.25 0.342105 0.333333 0.333333 0.282051 0.714286 0.394737 0.45 0.3 0.735294 0.567568 0.202703 0.297872 0.095238 0.735294 0.195652 0.191489 0.35 0.333333 0.282609 0.368421 0.044444 0.268293 0.1 0.086957 0.291667 0.065217 0.067797 0.045455 0.226415 0.023256 0.086957 1 0.292683 0.318182 0.292683 0.25 0.053571 0.058824 0.061224 0.276596 0.1 0.20339 0.325 0.318182 0.269231 0.107692 0.24 0.101695 0.292683 0.204082 0.021277 37 | D00950 0.416667 0.586207 0.433333 0.692308 0.580645 0.125 0.34375 0.292683 0.483871 0.342105 0.875 0.3 0.307692 0.171429 0.35 0.054054 0.3 0.333333 0.042553 0.566667 0.125 0.485714 0.451613 0.142857 0.451613 0.021277 0.075 0.486486 0.02439 0.076923 0.033333 0.288889 0.057143 0.04878 0.292683 1 0.5 1 0.472222 0.061224 0.048387 0.071429 0.394737 0.116279 0.185185 0.483871 0.59375 0.340909 0.083333 0.375 0.115385 0.769231 0.268293 0.051282 38 | D00951 0.473684 0.7 0.297297 0.580645 0.588235 0.190476 0.263158 0.318182 0.545455 0.365854 0.515152 0.357143 0.333333 0.242857 0.439024 0.157895 0.357143 0.261905 0.148936 0.485714 0.190476 0.540541 0.388889 0.097561 0.282051 0.177778 0.043478 0.428571 0.093023 0.070175 0.046875 0.465116 0.105263 0.043478 0.318182 0.5 1 0.5 0.487179 0.055556 0.029412 0.086957 0.487179 0.177778 0.169492 0.645161 0.555556 0.454545 0.147541 0.5 0.105263 0.378378 0.295455 0.022222 39 | D00954 0.416667 0.586207 0.433333 0.692308 0.580645 0.125 0.34375 0.292683 0.483871 0.342105 0.875 0.3 0.307692 0.171429 0.35 0.054054 0.3 0.333333 0.042553 0.566667 0.125 0.485714 0.451613 0.142857 0.451613 0.021277 0.075 0.486486 0.02439 0.076923 0.033333 0.288889 0.057143 0.04878 0.292683 1 0.5 1 0.472222 0.061224 0.048387 0.071429 0.394737 0.116279 0.185185 0.483871 0.59375 0.340909 0.083333 0.375 0.115385 0.769231 0.268293 0.051282 40 | D00956 0.414634 0.514286 0.470588 0.545455 0.435897 0.106383 0.470588 0.304348 0.394737 0.348837 0.529412 0.255319 0.318182 0.202703 0.326087 0.243243 0.255319 0.410256 0.12 0.384615 0.106383 0.372093 0.3 0.236842 0.405405 0.195652 0.162791 0.47619 0.113636 0.188679 0.112903 0.413043 0.257143 0.136364 0.25 0.472222 0.487179 0.472222 1 0.134615 0.125 0.181818 0.395349 0.25 0.290909 0.432432 0.487179 0.346939 0.180328 0.409091 0.181818 0.358974 0.439024 0.066667 41 | D00961 0.055556 0.061224 0.166667 0.041667 0.078431 0.02 0.195122 0.072727 0.083333 0.055556 0.0625 0.035714 0.055556 0.08642 0.090909 0.25 0.035714 0.227273 0.122449 0.06 0.02 0.054545 0.040816 0.15 0.186047 0.384615 0.361111 0.173077 0.411765 0.24 0.236364 0.163636 0.228571 0.324324 0.053571 0.061224 0.055556 0.061224 0.134615 1 0.245614 0.275 0.017241 0.317073 0.272727 0.061224 0.036364 0.083333 0.339623 0.070175 0.333333 0.061224 0.234043 0.044444 42 | D00962 0.076923 0.031746 0.107143 0.05 0.0625 0.103448 0.127273 0.090909 0.048387 0.111111 0.032258 0.044118 0.076923 0.074468 0.089552 0.208333 0.044118 0.116667 0.133333 0.064516 0.103448 0.028986 0.04918 0.156863 0.122807 0.288462 0.347826 0.138462 0.150943 0.388889 0.306452 0.132353 0.191489 0.347826 0.058824 0.048387 0.029412 0.048387 0.125 0.245614 1 0.142857 0.028571 0.218182 0.185714 0.031746 0.029412 0.068493 0.235294 0.072464 0.305085 0.048387 0.20339 0.2 43 | D00965 0.041667 0.097561 0.3125 0.102564 0.090909 0.073171 0.235294 0.061224 0.046512 0.041667 0.1 0.040816 0.041667 0.140845 0.081633 0.225806 0.040816 0.305556 0.142857 0.121951 0.073171 0.085106 0.047619 0.344828 0.222222 0.205128 0.2 0.2 0.576923 0.222222 0.12963 0.1875 0.2 0.2 0.061224 0.071429 0.086957 0.071429 0.181818 0.275 0.142857 1 0.083333 0.205128 0.211538 0.097561 0.041667 0.09434 0.142857 0.102041 0.14 0.022727 0.243902 0.025641 44 | D01115 0.348837 0.472222 0.25 0.457143 0.473684 0.155556 0.219512 0.276596 0.394737 0.348837 0.405405 0.282609 0.288889 0.202703 0.355556 0.121951 0.282609 0.222222 0.098039 0.542857 0.155556 0.685714 0.444444 0.093023 0.238095 0.1 0.041667 0.409091 0.042553 0.067797 0.045455 0.413043 0.1 0.041667 0.276596 0.394737 0.487179 0.394737 0.395349 0.017241 0.028571 0.083333 1 0.170213 0.145161 0.432432 0.414634 0.434783 0.107692 0.44186 0.065574 0.325 0.255319 0.021277 45 | D01132 0.104167 0.116279 0.25 0.121951 0.108696 0.146341 0.25 0.1 0.116279 0.104167 0.119048 0.102041 0.104167 0.166667 0.12 0.413793 0.102041 0.219512 0.275 0.113636 0.146341 0.102041 0.119048 0.166667 0.27027 0.428571 0.40625 0.23913 0.222222 0.26087 0.230769 0.22449 0.344828 0.363636 0.1 0.116279 0.177778 0.116279 0.25 0.317073 0.218182 0.205128 0.170213 1 0.178571 0.116279 0.204545 0.150943 0.313725 0.1875 0.304348 0.116279 0.285714 0.04878 46 | D01161 0.277778 0.207547 0.386364 0.24 0.218182 0.067797 0.452381 0.267857 0.230769 0.277778 0.211538 0.166667 0.232143 0.136364 0.2 0.14 0.186441 0.375 0.098361 0.203704 0.067797 0.186441 0.211538 0.09434 0.4 0.137931 0.173077 0.237288 0.2 0.138462 0.126761 0.225806 0.170213 0.150943 0.20339 0.185185 0.169492 0.185185 0.290909 0.272727 0.185714 0.211538 0.145161 0.178571 1 0.207547 0.169492 0.166667 0.202899 0.177419 0.245902 0.185185 0.458333 0.282609 47 | D01217 0.5 0.84 0.34375 0.692308 0.580645 0.184211 0.34375 0.325 0.703704 0.416667 0.5 0.333333 0.342105 0.205882 0.421053 0.083333 0.333333 0.297297 0.065217 0.516129 0.184211 0.625 0.40625 0.111111 0.285714 0.090909 0.04878 0.375 0.105263 0.076923 0.050847 0.318182 0.027778 0.04878 0.325 0.483871 0.645161 0.483871 0.432432 0.061224 0.031746 0.097561 0.432432 0.116279 0.207547 1 0.416667 0.372093 0.140351 0.375 0.115385 0.352941 0.268293 0.025 48 | D01294 0.4 0.5 0.297297 0.484848 0.421053 0.162791 0.297297 0.288889 0.457143 0.365854 0.612903 0.295455 0.302326 0.191781 0.311111 0.128205 0.325581 0.261905 0.102041 0.405405 0.162791 0.425 0.388889 0.046512 0.388889 0.104167 0.043478 0.395349 0.044444 0.033898 0.046875 0.3125 0.105263 0.043478 0.318182 0.59375 0.555556 0.59375 0.487179 0.036364 0.029412 0.041667 0.380952 0.204545 0.169492 0.416667 1 0.391304 0.111111 0.463415 0.067797 0.545455 0.325581 0.022222 49 | D01387 0.306122 0.404762 0.244444 0.390244 0.47619 0.137255 0.217391 0.294118 0.372093 0.306122 0.318182 0.27451 0.306122 0.266667 0.634146 0.155556 0.27451 0.196078 0.107143 0.463415 0.137255 0.354167 0.348837 0.081633 0.208333 0.150943 0.056604 0.333333 0.1 0.078125 0.056338 0.613636 0.111111 0.056604 0.269231 0.340909 0.454545 0.340909 0.346939 0.083333 0.068493 0.09434 0.434783 0.150943 0.166667 0.372093 0.361702 1 0.130435 0.545455 0.126984 0.282609 0.27451 0.038462 50 | D01441 0.129032 0.140351 0.215686 0.105263 0.133333 0.084746 0.215686 0.125 0.140351 0.129032 0.084746 0.109375 0.129032 0.16092 0.140625 0.183673 0.109375 0.264151 0.133333 0.118644 0.084746 0.126984 0.122807 0.113208 0.207547 0.288462 0.24 0.213115 0.22 0.171875 0.372881 0.184615 0.142857 0.24 0.107692 0.083333 0.147541 0.083333 0.180328 0.314815 0.217391 0.142857 0.107692 0.313725 0.202899 0.140351 0.111111 0.130435 1 0.121212 0.45283 0.083333 0.20339 0.034483 51 | D01689 0.363636 0.447368 0.3 0.472222 0.526316 0.148936 0.268293 0.265306 0.375 0.304348 0.384615 0.244898 0.276596 0.246575 0.575 0.2 0.244898 0.23913 0.137255 0.513514 0.148936 0.386364 0.35 0.088889 0.285714 0.14 0.083333 0.361702 0.0625 0.083333 0.075758 0.595238 0.15 0.061224 0.24 0.375 0.5 0.375 0.409091 0.070175 0.072464 0.102041 0.44186 0.1875 0.177419 0.375 0.463415 0.545455 0.121212 1 0.116667 0.309524 0.386364 0.06383 52 | D02217 0.125 0.115385 0.25 0.098039 0.150943 0.055556 0.27907 0.140351 0.137255 0.145455 0.096154 0.084746 0.125 0.189873 0.157895 0.186047 0.084746 0.2 0.12963 0.134615 0.055556 0.103448 0.096154 0.130435 0.266667 0.304348 0.341463 0.196429 0.2 0.259259 0.254237 0.166667 0.195122 0.309524 0.101695 0.115385 0.105263 0.115385 0.181818 0.333333 0.327586 0.14 0.065574 0.304348 0.245902 0.115385 0.067797 0.126984 0.45283 0.116667 1 0.115385 0.254902 0.039216 53 | D02367 0.378378 0.4375 0.30303 0.517241 0.484848 0.125 0.34375 0.325 0.4375 0.342105 0.666667 0.333333 0.342105 0.15493 0.285714 0.054054 0.333333 0.230769 0.042553 0.46875 0.125 0.368421 0.451613 0.081081 0.40625 0.021277 0.075 0.410256 0.02439 0.037037 0.033333 0.234043 0.057143 0.04878 0.292683 0.769231 0.378378 0.769231 0.358974 0.061224 0.048387 0.022727 0.325 0.116279 0.185185 0.352941 0.545455 0.282609 0.083333 0.309524 0.115385 1 0.238095 0.051282 54 | D04066 0.295455 0.3 0.53125 0.351351 0.27907 0.085106 0.633333 0.255319 0.268293 0.295455 0.307692 0.208333 0.266667 0.157895 0.276596 0.25 0.208333 0.459459 0.170213 0.292683 0.085106 0.208333 0.307692 0.121951 0.545455 0.2 0.195122 0.326087 0.230769 0.169811 0.152542 0.361702 0.264706 0.166667 0.204082 0.268293 0.295455 0.268293 0.439024 0.234043 0.20339 0.243902 0.255319 0.285714 0.458333 0.268293 0.325581 0.27451 0.20339 0.386364 0.254902 0.238095 1 0.119048 55 | D05341 0.095238 0.025 0.027027 0.026316 0.047619 0.111111 0.027027 0.090909 0.025 0.15 0.025641 0.021739 0.095238 0.013158 0.088889 0.0625 0.021739 0.02381 0.047619 0.02439 0.111111 0.021739 0.025641 0.060606 0.025641 0.02381 0.085714 0.020408 0.027778 0.159091 0.14 0.039216 0.142857 0.027027 0.021277 0.051282 0.022222 0.051282 0.066667 0.044444 0.2 0.025641 0.021277 0.04878 0.282609 0.025 0.022222 0.038462 0.034483 0.06383 0.039216 0.051282 0.119048 1 56 | -------------------------------------------------------------------------------- /datasets/nr_simmat_dg.txt: -------------------------------------------------------------------------------- 1 | hsa190 hsa2099 hsa2100 hsa2101 hsa2103 hsa2104 hsa2908 hsa3174 hsa367 hsa4306 hsa5241 hsa5465 hsa5468 hsa5914 hsa5915 hsa5916 hsa6095 hsa6096 hsa6097 hsa6256 hsa6257 hsa6258 hsa7421 hsa8856 hsa9970 hsa9971 2 | hsa190 1 0.0260267330473769 0.0251995975656403 0.0419509905417681 0.0427595980366958 0.0518851146950085 0.0191142136435537 0.067201228297951 0.0192123820228085 0.0203468746364678 0.0207717584404241 0.0273345843851285 0.0284547955771943 0.0250328076662088 0.0237477344356753 0.0249774256325095 0.0179332965382288 0.020343388183645 0.0268482202884501 0.0709573569384697 0.0628826556873632 0.0369390417548288 0.0371596973450943 0.0351697092472271 0.0287873801678091 0.0360788430599661 3 | hsa2099 0.0260267330473769 1 0.385291520838268 0.254100147044773 0.241851027557274 0.258893783071742 0.131047708158589 0.140771091937798 0.119238019840392 0.120272044897541 0.129516860347121 0.0928416059632321 0.109252441521706 0.140516447774696 0.148842576683781 0.148462200221682 0.0853387890059742 0.0907107607976082 0.0810574896203805 0.182938949807791 0.168555206268866 0.0386844362636134 0.11238399175576 0.095273230694617 0.127434733513351 0.117583866840084 4 | hsa2100 0.0251995975656403 0.385291520838268 1 0.239561435273825 0.226572973058899 0.240141229829559 0.144458467816554 0.148144238837628 0.119956966479618 0.122777591813845 0.131196485470263 0.0961173005583893 0.0944790521170864 0.148962852942309 0.141943831335285 0.145227111218421 0.0886474805186148 0.0952939816590417 0.0869836228307792 0.16852812709186 0.15788155102036 0.0366355565472728 0.102551371872857 0.0887873905929847 0.13704416494424 0.12692056768406 5 | hsa2101 0.0419509905417681 0.254100147044773 0.239561435273825 1 0.50963752565178 0.545147367118954 0.154411534459839 0.184967167018432 0.124191806468867 0.136918210708971 0.142383357449572 0.11005572899274 0.128351667703708 0.188524604309974 0.183204980814741 0.195533252952748 0.0949960169480974 0.10275137859612 0.0927662474913186 0.236719914221088 0.216986349497146 0.038773270945947 0.138295014055253 0.124020711581926 0.169994059397339 0.136654880970634 6 | hsa2103 0.0427595980366958 0.241851027557274 0.226572973058899 0.50963752565178 1 0.72118593374241 0.144227170174578 0.190795358652709 0.110717515672762 0.114967307197784 0.133362966690369 0.123747169691191 0.126886718226598 0.177806631347855 0.183425183423112 0.20802618751273 0.0917355367128298 0.0962211945717274 0.0931881095233227 0.232586661248195 0.206649656550772 0.0376573723308501 0.14118775181612 0.112519169112861 0.157941243147555 0.14022485455954 7 | hsa2104 0.0518851146950085 0.258893783071742 0.240141229829559 0.545147367118954 0.72118593374241 1 0.161482721036549 0.19965204092503 0.119156991913079 0.130254176101968 0.140475251842776 0.14240658712667 0.142440357476068 0.215258858304074 0.206061539255472 0.231099692363152 0.0946959150190497 0.100863331679184 0.0939674210449986 0.250417819756327 0.216729186443535 0.0408015113159692 0.142766050057796 0.115004246729531 0.161980128306721 0.153875566327121 8 | hsa2908 0.0191142136435537 0.131047708158589 0.144458467816554 0.154411534459839 0.144227170174578 0.161482721036549 1 0.10292202319282 0.23366133237194 0.276680673701978 0.257273185997517 0.0770235782318696 0.0680350221098536 0.0956682219808598 0.0967512260194818 0.099896408066952 0.061022018367731 0.0653405266697028 0.0632340591109719 0.114512424231549 0.101278583187301 0.0424742454395567 0.0641000255241269 0.0716087871365802 0.107362639938087 0.0922508840782246 9 | hsa3174 0.067201228297951 0.140771091937798 0.148144238837628 0.184967167018432 0.190795358652709 0.19965204092503 0.10292202319282 1 0.0931003191540263 0.0823122689971206 0.097153782604896 0.132132232529795 0.13124123054992 0.174741650897043 0.18903472256636 0.179164431115118 0.0977090488244506 0.107429890897985 0.102083125656298 0.300161966515045 0.278860573509804 0.0352477378441716 0.119420545127527 0.115132995433364 0.152691480962303 0.130524007330819 10 | hsa367 0.0192123820228085 0.119238019840392 0.119956966479618 0.124191806468867 0.110717515672762 0.119156991913079 0.23366133237194 0.0931003191540263 1 0.205292729861636 0.254715034157448 0.0614301755419609 0.0638462018276166 0.0872173108056878 0.0845598171313611 0.0910417177834616 0.0556788506823852 0.0607664129009518 0.0572738028793646 0.104676668165327 0.09363641311181 0.0283280026621643 0.06786857138423 0.0648460913023208 0.0877870079466815 0.0816834732082478 11 | hsa4306 0.0203468746364678 0.120272044897541 0.122777591813845 0.136918210708971 0.114967307197784 0.130254176101968 0.276680673701978 0.0823122689971206 0.205292729861636 1 0.230991199722466 0.0699413282975481 0.0667950739719785 0.0870932644100805 0.0849722432081885 0.0955033020363903 0.0610715808774633 0.0713580139979585 0.0648467659555655 0.0905693724095082 0.0820310293987481 0.0359637753775175 0.0761895685964624 0.0756135298878157 0.0940189670678044 0.0855913272705694 12 | hsa5241 0.0207717584404241 0.129516860347121 0.131196485470263 0.142383357449572 0.133362966690369 0.140475251842776 0.257273185997517 0.097153782604896 0.254715034157448 0.230991199722466 1 0.0592770028809637 0.0697549037809539 0.084890804176111 0.0782779662622246 0.087115914431307 0.0578200426536533 0.0624412400024206 0.0573597351150553 0.10415882731011 0.0966434458173313 0.0358803449581123 0.0600288410213121 0.0663300763081153 0.0926007835506728 0.0775708396071611 13 | hsa5465 0.0273345843851285 0.0928416059632321 0.0961173005583893 0.11005572899274 0.123747169691191 0.14240658712667 0.0770235782318696 0.132132232529795 0.0614301755419609 0.0699413282975481 0.0592770028809637 1 0.544391219957297 0.201525923974561 0.212327748029733 0.203272953553013 0.16545072083858 0.177732853233981 0.141836052636501 0.17513614812897 0.147765914052046 0.0320060487928147 0.1632571018396 0.146106237092178 0.12526925840366 0.152315629337298 14 | hsa5468 0.0284547955771943 0.109252441521706 0.0944790521170864 0.128351667703708 0.126886718226598 0.142440357476068 0.0680350221098536 0.13124123054992 0.0638462018276166 0.0667950739719785 0.0697549037809539 0.544391219957297 1 0.194659564616783 0.202030064177239 0.198253005352371 0.17143731923535 0.186253055455728 0.155260602537649 0.170568120819154 0.143658083581871 0.0295084329800469 0.182382807758855 0.162154222095413 0.12510122576842 0.17152843782458 15 | hsa5914 0.0250328076662088 0.140516447774696 0.148962852942309 0.188524604309974 0.177806631347855 0.215258858304074 0.0956682219808598 0.174741650897043 0.0872173108056878 0.0870932644100805 0.084890804176111 0.201525923974561 0.194659564616783 1 0.728869042945886 0.694135045382142 0.151815976147657 0.16340232159034 0.125573385188487 0.233392037889312 0.211265291546024 0.053073234179177 0.174975639849524 0.167252819113473 0.175003406241316 0.189036376549229 16 | hsa5915 0.0237477344356753 0.148842576683781 0.141943831335285 0.183204980814741 0.183425183423112 0.206061539255472 0.0967512260194818 0.18903472256636 0.0845598171313611 0.0849722432081885 0.0782779662622246 0.212327748029733 0.202030064177239 0.728869042945886 1 0.718698414016836 0.171634991706759 0.178881876391 0.132404461463115 0.234236863010801 0.21111625020399 0.0422928995299998 0.180287371234053 0.174372335985404 0.175534239642375 0.19755222821516 17 | hsa5916 0.0249774256325095 0.148462200221682 0.145227111218421 0.195533252952748 0.20802618751273 0.231099692363152 0.099896408066952 0.179164431115118 0.0910417177834616 0.0955033020363903 0.087115914431307 0.203272953553013 0.198253005352371 0.694135045382142 0.718698414016836 1 0.162775816306466 0.17670864336501 0.130248849939608 0.242853806826029 0.218695594732799 0.0476602345097285 0.18310795361604 0.160388379284729 0.182370330482034 0.19161246985935 18 | hsa6095 0.0179332965382288 0.0853387890059742 0.0886474805186148 0.0949960169480974 0.0917355367128298 0.0946959150190497 0.061022018367731 0.0977090488244506 0.0556788506823852 0.0610715808774633 0.0578200426536533 0.16545072083858 0.17143731923535 0.151815976147657 0.171634991706759 0.162775816306466 1 0.578460678512331 0.430084893083516 0.116272100888818 0.103159990851818 0.0380212263629869 0.163294306672937 0.138406457842664 0.104977512969887 0.135811023936828 19 | hsa6096 0.020343388183645 0.0907107607976082 0.0952939816590417 0.10275137859612 0.0962211945717274 0.100863331679184 0.0653405266697028 0.107429890897985 0.0607664129009518 0.0713580139979585 0.0624412400024206 0.177732853233981 0.186253055455728 0.16340232159034 0.178881876391 0.17670864336501 0.578460678512331 1 0.458057156550255 0.12494160702936 0.110785386941785 0.0346374908025839 0.161151423011429 0.147544739617685 0.116166132035529 0.144855627728995 20 | hsa6097 0.0268482202884501 0.0810574896203805 0.0869836228307792 0.0927662474913186 0.0931881095233227 0.0939674210449986 0.0632340591109719 0.102083125656298 0.0572738028793646 0.0648467659555655 0.0573597351150553 0.141836052636501 0.155260602537649 0.125573385188487 0.132404461463115 0.130248849939608 0.430084893083516 0.458057156550255 1 0.12000937748989 0.109785830862722 0.0352917490930767 0.143723144988282 0.156083549953203 0.105400931197879 0.108266122031717 21 | hsa6256 0.0709573569384697 0.182938949807791 0.16852812709186 0.236719914221088 0.232586661248195 0.250417819756327 0.114512424231549 0.300161966515045 0.104676668165327 0.0905693724095082 0.10415882731011 0.17513614812897 0.170568120819154 0.233392037889312 0.234236863010801 0.242853806826029 0.116272100888818 0.12494160702936 0.12000937748989 1 0.622798925969169 0.0581965392383344 0.130656888227073 0.126320184020689 0.151055920511414 0.158001611365483 22 | hsa6257 0.0628826556873632 0.168555206268866 0.15788155102036 0.216986349497146 0.206649656550772 0.216729186443535 0.101278583187301 0.278860573509804 0.09363641311181 0.0820310293987481 0.0966434458173313 0.147765914052046 0.143658083581871 0.211265291546024 0.21111625020399 0.218695594732799 0.103159990851818 0.110785386941785 0.109785830862722 0.622798925969169 1 0.0443200511579712 0.109840297961348 0.116767356777147 0.127375149138857 0.136586768778264 23 | hsa6258 0.0369390417548288 0.0386844362636134 0.0366355565472728 0.038773270945947 0.0376573723308501 0.0408015113159692 0.0424742454395567 0.0352477378441716 0.0283280026621643 0.0359637753775175 0.0358803449581123 0.0320060487928147 0.0295084329800469 0.053073234179177 0.0422928995299998 0.0476602345097285 0.0380212263629869 0.0346374908025839 0.0352917490930767 0.0581965392383344 0.0443200511579712 1 0.0431599435664482 0.0488061533503682 0.0329580792649681 0.036481016479963 24 | hsa7421 0.0371596973450943 0.11238399175576 0.102551371872857 0.138295014055253 0.14118775181612 0.142766050057796 0.0641000255241269 0.119420545127527 0.06786857138423 0.0761895685964624 0.0600288410213121 0.1632571018396 0.182382807758855 0.174975639849524 0.180287371234053 0.18310795361604 0.163294306672937 0.161151423011429 0.143723144988282 0.130656888227073 0.109840297961348 0.0431599435664482 1 0.358626657836617 0.272158931275929 0.202261064678409 25 | hsa8856 0.0351697092472271 0.095273230694617 0.0887873905929847 0.124020711581926 0.112519169112861 0.115004246729531 0.0716087871365802 0.115132995433364 0.0648460913023208 0.0756135298878157 0.0663300763081153 0.146106237092178 0.162154222095413 0.167252819113473 0.174372335985404 0.160388379284729 0.138406457842664 0.147544739617685 0.156083549953203 0.126320184020689 0.116767356777147 0.0488061533503682 0.358626657836617 1 0.324824642311919 0.189048691799508 26 | hsa9970 0.0287873801678091 0.127434733513351 0.13704416494424 0.169994059397339 0.157941243147555 0.161980128306721 0.107362639938087 0.152691480962303 0.0877870079466815 0.0940189670678044 0.0926007835506728 0.12526925840366 0.12510122576842 0.175003406241316 0.175534239642375 0.182370330482034 0.104977512969887 0.116166132035529 0.105400931197879 0.151055920511414 0.127375149138857 0.0329580792649681 0.272158931275929 0.324824642311919 1 0.190025334623086 27 | hsa9971 0.0360788430599661 0.117583866840084 0.12692056768406 0.136654880970634 0.14022485455954 0.153875566327121 0.0922508840782246 0.130524007330819 0.0816834732082478 0.0855913272705694 0.0775708396071611 0.152315629337298 0.17152843782458 0.189036376549229 0.19755222821516 0.19161246985935 0.135811023936828 0.144855627728995 0.108266122031717 0.158001611365483 0.136586768778264 0.036481016479963 0.202261064678409 0.189048691799508 0.190025334623086 1 28 | -------------------------------------------------------------------------------- /functions.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | import numpy as np 4 | from collections import defaultdict 5 | 6 | 7 | def load_data_from_file(dataset, folder): 8 | with open(os.path.join(folder, dataset+"_admat_dgc.txt"), "r") as inf: 9 | inf.next() 10 | int_array = [line.strip("\n").split()[1:] for line in inf] 11 | 12 | with open(os.path.join(folder, dataset+"_simmat_dc.txt"), "r") as inf: # the drug similarity file 13 | inf.next() 14 | drug_sim = [line.strip("\n").split()[1:] for line in inf] 15 | 16 | with open(os.path.join(folder, dataset+"_simmat_dg.txt"), "r") as inf: # the target similarity file 17 | inf.next() 18 | target_sim = [line.strip("\n").split()[1:] for line in inf] 19 | 20 | intMat = np.array(int_array, dtype=np.float64).T # drug-target interaction matrix 21 | drugMat = np.array(drug_sim, dtype=np.float64) # drug similarity matrix 22 | targetMat = np.array(target_sim, dtype=np.float64) # target similarity matrix 23 | return intMat, drugMat, targetMat 24 | 25 | 26 | def get_drugs_targets_names(dataset, folder): 27 | with open(os.path.join(folder, dataset+"_admat_dgc.txt"), "r") as inf: 28 | drugs = inf.next().strip("\n").split() 29 | targets = [line.strip("\n").split()[0] for line in inf] 30 | return drugs, targets 31 | 32 | 33 | def cross_validation(intMat, seeds, cv=0, num=10): 34 | cv_data = defaultdict(list) 35 | for seed in seeds: 36 | num_drugs, num_targets = intMat.shape 37 | prng = np.random.RandomState(seed) 38 | if cv == 0: 39 | index = prng.permutation(num_drugs) 40 | if cv == 1: 41 | index = prng.permutation(intMat.size) 42 | step = index.size/num 43 | for i in xrange(num): 44 | if i < num-1: 45 | ii = index[i*step:(i+1)*step] 46 | else: 47 | ii = index[i*step:] 48 | if cv == 0: 49 | test_data = np.array([[k, j] for k in ii for j in xrange(num_targets)], dtype=np.int32) 50 | elif cv == 1: 51 | test_data = np.array([[k/num_targets, k % num_targets] for k in ii], dtype=np.int32) 52 | x, y = test_data[:, 0], test_data[:, 1] 53 | test_label = intMat[x, y] 54 | W = np.ones(intMat.shape) 55 | W[x, y] = 0 56 | cv_data[seed].append((W, test_data, test_label)) 57 | return cv_data 58 | 59 | 60 | def train(model, cv_data, intMat, drugMat, targetMat): 61 | aupr, auc = [], [] 62 | for seed in cv_data.keys(): 63 | for W, test_data, test_label in cv_data[seed]: 64 | model.fix_model(W, intMat, drugMat, targetMat, seed) 65 | aupr_val, auc_val = model.evaluation(test_data, test_label) 66 | aupr.append(aupr_val) 67 | auc.append(auc_val) 68 | return np.array(aupr, dtype=np.float64), np.array(auc, dtype=np.float64) 69 | 70 | 71 | def svd_init(M, num_factors): 72 | from scipy.linalg import svd 73 | U, s, V = svd(M, full_matrices=False) 74 | ii = np.argsort(s)[::-1][:num_factors] 75 | s1 = np.sqrt(np.diag(s[ii])) 76 | U0, V0 = U[:, ii].dot(s1), s1.dot(V[ii, :]) 77 | return U0, V0.T 78 | 79 | 80 | def mean_confidence_interval(data, confidence=0.95): 81 | import scipy as sp 82 | import scipy.stats 83 | a = 1.0*np.array(data) 84 | n = len(a) 85 | m, se = np.mean(a), scipy.stats.sem(a) 86 | h = se * sp.stats.t._ppf((1+confidence)/2., n-1) 87 | return m, h 88 | 89 | 90 | def write_metric_vector_to_file(auc_vec, file_name): 91 | np.savetxt(file_name, auc_vec, fmt='%.6f') 92 | 93 | 94 | def load_metric_vector(file_name): 95 | return np.loadtxt(file_name, dtype=np.float64) 96 | -------------------------------------------------------------------------------- /kbmf.py: -------------------------------------------------------------------------------- 1 | 2 | ''' 3 | [1] M. Gonen, "Predicting drug-target interactions from chemical and genomical kernels using Bayesian matrix factorization, Bioinformatics, 2012" 4 | ''' 5 | 6 | import os 7 | import numpy as np 8 | from pymatbridge import Matlab 9 | from sklearn.metrics import precision_recall_curve, roc_curve 10 | from sklearn.metrics import auc 11 | 12 | 13 | class KBMF: 14 | 15 | def __init__(self, num_factors=10): 16 | self.num_factors = num_factors 17 | 18 | def fix_model(self, W, intMat, drugMat, targetMat, seed=None): 19 | R = W*intMat 20 | drugMat = (drugMat+drugMat.T)/2 21 | targetMat = (targetMat+targetMat.T)/2 22 | mlab = Matlab() 23 | mlab.start() 24 | # print os.getcwd() 25 | # self.predictR = mlab.run_func(os.sep.join([os.getcwd(), "kbmf2k", "kbmf.m"]), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result'] 26 | self.predictR = mlab.run_func(os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"])), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result'] 27 | # print os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"])) 28 | mlab.stop() 29 | 30 | def predict_scores(self, test_data, N): 31 | inx = np.array(test_data) 32 | return self.predictR[inx[:, 0], inx[:, 1]] 33 | 34 | def evaluation(self, test_data, test_label): 35 | scores = self.predictR[test_data[:, 0], test_data[:, 1]] 36 | prec, rec, thr = precision_recall_curve(test_label, scores) 37 | aupr_val = auc(rec, prec) 38 | fpr, tpr, thr = roc_curve(test_label, scores) 39 | auc_val = auc(fpr, tpr) 40 | return aupr_val, auc_val 41 | 42 | def __str__(self): 43 | return "Model: KBMF, num_factors:%s" % (self.num_factors) 44 | -------------------------------------------------------------------------------- /netlaprls.py: -------------------------------------------------------------------------------- 1 | ''' 2 | [1] Xia, Zheng, et al. "Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces." BMC systems biology 4.Suppl 2 (2010): S6. 3 | 4 | Default parameters in [1]: 5 | gamma_d1 = gamma_p1 = 1 6 | gamma_d2 = gamma_p2 = 0.01 7 | beta_d = beta_p = 0.3 8 | Default parameters in this implementation: 9 | gamma_d = 0.01, gamma_d=gamma_d2/gamma_d1 10 | gamma_t = 0.01, gamma_t=gamma_p2/gamma_p1 11 | beta_d = 0.3 12 | beta_t = 0.3 13 | ''' 14 | import numpy as np 15 | from sklearn.metrics import precision_recall_curve, roc_curve 16 | from sklearn.metrics import auc 17 | 18 | 19 | class NetLapRLS: 20 | 21 | def __init__(self, gamma_d=0.01, gamma_t=0.01, beta_d=0.3, beta_t=0.3): 22 | self.gamma_d = float(gamma_d) 23 | self.gamma_t = float(gamma_t) 24 | self.beta_d = float(beta_d) 25 | self.beta_t = float(beta_t) 26 | 27 | def fix_model(self, W, intMat, drugMat, targetMat, seed=None): 28 | R = W*intMat 29 | m, n = R.shape 30 | drugMat = (drugMat+drugMat.T)/2 31 | targetMat = (targetMat+targetMat.T)/2 32 | Wd = (drugMat+self.gamma_d*np.dot(R, R.T))/(1.0+self.gamma_d) 33 | Wt = (targetMat+self.gamma_t*np.dot(R.T, R))/(1.0+self.gamma_t) 34 | Wd = Wd-np.diag(np.diag(Wd)) 35 | Wt = Wt-np.diag(np.diag(Wt)) 36 | 37 | D = np.diag(np.sqrt(1.0/np.sum(Wd, axis=1))) 38 | Ld = np.eye(m) - np.dot(np.dot(D, Wd), D) 39 | D = np.diag(np.sqrt(1.0/np.sum(Wt, axis=1))) 40 | Lt = np.eye(n) - np.dot(np.dot(D, Wt), D) 41 | 42 | X = np.linalg.inv(Wd+self.beta_d*np.dot(Ld, Wd)) 43 | Fd = np.dot(np.dot(Wd, X), R) 44 | X = np.linalg.inv(Wt+self.beta_t*np.dot(Lt, Wt)) 45 | Ft = np.dot(np.dot(Wt, X), R.T) 46 | self.predictR = 0.5*(Fd+Ft.T) 47 | 48 | def predict_scores(self, test_data, N): 49 | inx = np.array(test_data) 50 | return self.predictR[inx[:, 0], inx[:, 1]] 51 | 52 | def evaluation(self, test_data, test_label): 53 | scores = self.predictR[test_data[:, 0], test_data[:, 1]] 54 | prec, rec, thr = precision_recall_curve(test_label, scores) 55 | aupr_val = auc(rec, prec) 56 | fpr, tpr, thr = roc_curve(test_label, scores) 57 | auc_val = auc(fpr, tpr) 58 | return aupr_val, auc_val 59 | 60 | def __str__(self): 61 | return "Model: NetLapRLS, gamma_d:%s, gamma_t:%s, beta_d:%s, beta_t:%s" % (self.gamma_d, self.gamma_t, self.beta_d, self.beta_t) 62 | -------------------------------------------------------------------------------- /new_pairs.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | import numpy as np 4 | # from functions import * 5 | from collections import defaultdict 6 | 7 | 8 | def load_drug_target_pairs(file_path): 9 | int_pairs = defaultdict(list) 10 | with open(file_path, "r") as inf: 11 | for line in inf: 12 | data = line.strip('\n').split() 13 | if len(data) > 1: 14 | int_pairs[data[0]] = data[1:] 15 | return int_pairs 16 | 17 | 18 | def load_metador_interaction_data(file_path): 19 | matador = defaultdict(list) 20 | with open(file_path, "r") as inf: 21 | inf.next() 22 | for line in inf: 23 | data = line.strip('\n').split('\t') 24 | matador[data[0]].extend(data[6].split()) 25 | return matador 26 | 27 | 28 | def load_keggid_map(database_folder): 29 | pubMap = {} 30 | with open(os.path.join(database_folder, "pubchem_SIDs_to_CIDs.txt"), "r") as inf: 31 | inf.next() 32 | for line in inf: 33 | sid, cid = line.strip('\n').split() 34 | pubMap[sid] = cid 35 | 36 | drugMap = defaultdict(dict) 37 | with open(os.path.join(database_folder, "kegg_drug_map.txt"), "r") as inf: 38 | inf.next() 39 | for line in inf: 40 | k, d, c, p, ch = line.strip('\n').split('$') 41 | if d != " ": 42 | drugMap[k]["drugbank"] = d.split() 43 | if c != " ": 44 | drugMap[k]["CHEMBL"] = c.split() 45 | if p != " ": 46 | drugMap[k]["PubChemCID"] = [pubMap[sid] for sid in p.split() if sid in pubMap] 47 | 48 | targetMap = defaultdict(dict) 49 | with open(os.path.join(database_folder, "target_kegg_uniprot.txt"), "r") as inf: 50 | inf.next() 51 | for line in inf: 52 | data = line.strip().split() 53 | if len(data) > 1: 54 | targetMap[data[0]]["uniprot"] = data[1:] 55 | with open(os.path.join(database_folder, "target_kegg_chembl.txt"), "r") as inf: 56 | inf.next() 57 | for line in inf: 58 | data = line.strip().split() 59 | if len(data) > 1: 60 | targetMap[data[0]]["chembl"] = data[1:] 61 | return drugMap, targetMap 62 | 63 | 64 | def verify_drug_target_interactions(pairs, kegg, drugbank, chembl, matador, drugMap, targetMap, output_file): 65 | conf_pairs = [] 66 | outf = open(output_file, "w") 67 | for num in xrange(len(pairs)): 68 | d, t, v = pairs[num] 69 | if d in kegg: 70 | if t in kegg[d]: 71 | k_value = 1 72 | else: 73 | k_value = 0 74 | else: 75 | k_value = 0 76 | try: 77 | x = 0 78 | for dm in drugMap[d]["drugbank"]: 79 | for t1 in targetMap[t]["uniprot"]: 80 | if t1 in drugbank[dm]: 81 | x += 1 82 | if x > 0: 83 | d_value = 1 84 | else: 85 | d_value = 0 86 | except: 87 | d_value = 0 88 | try: 89 | x = 0 90 | for dm in drugMap[d]["CHEMBL"]: 91 | for t1 in targetMap[t]["chembl"]: 92 | if t1 in chembl[dm]: 93 | x += 1 94 | if x > 0: 95 | c_value = 1 96 | else: 97 | c_value = 0 98 | except: 99 | c_value = 0 100 | 101 | try: 102 | x = 0 103 | for dm in drugMap[d]["PubChemCID"]: 104 | for t1 in targetMap[t]["uniprot"]: 105 | if t1 in matador[dm]: 106 | x += 1 107 | if x > 0: 108 | m_value = 1 109 | else: 110 | m_value = 0 111 | except: 112 | m_value = 0 113 | if k_value == 1: 114 | k_str = "K" 115 | else: 116 | k_str = " " 117 | 118 | if c_value == 1: 119 | c_str = "C" 120 | else: 121 | c_str = " " 122 | 123 | if d_value == 1: 124 | d_str = "D" 125 | else: 126 | d_str = " " 127 | 128 | if m_value == 1: 129 | m_str = "M" 130 | else: 131 | m_str = " " 132 | num += 1 133 | if k_value+d_value+c_value+m_value > 0: 134 | conf_pairs.append((d, t, 1)) 135 | else: 136 | conf_pairs.append((d, t, 0)) 137 | outf.write("\t\t".join([str(num), d, t, str(np.round(v, 4)), c_str, d_str, k_str, m_str])+"\n") 138 | outf.close() 139 | return conf_pairs 140 | 141 | 142 | def novel_prediction_analysis(predict_pairs, output_file, database_folder, positions=[10, 30, 50, 100, 200, 500, 1000]): 143 | drugMap, targetMap = load_keggid_map(database_folder) 144 | kegg = load_drug_target_pairs(os.path.join(database_folder, "kegg.txt")) 145 | drugbank = load_drug_target_pairs(os.path.join(database_folder, "drugbank.txt")) 146 | cheml = load_drug_target_pairs(os.path.join(database_folder, "chembl.txt")) 147 | matador = load_metador_interaction_data(os.path.join(database_folder, "matador.tsv")) 148 | verify_pairs = verify_drug_target_interactions(predict_pairs, kegg, drugbank, cheml, matador, drugMap, targetMap, output_file) 149 | inx = np.array(positions) 150 | vec = np.zeros(inx.size) 151 | num_pairs = len(verify_pairs) 152 | for i in xrange(num_pairs): 153 | d, t, v = verify_pairs[i] 154 | if v > 0: 155 | ii = ((i+1) <= inx) 156 | vec[ii] += 1.0 157 | for i, p in enumerate(positions): 158 | if p <= num_pairs: 159 | print "Top-%s novel DTIs, NO. confirmed:%s, Percentage:%.2f%%" % (p, int(vec[i]), vec[i]*100/inx[i]) 160 | -------------------------------------------------------------------------------- /nrlmf.py: -------------------------------------------------------------------------------- 1 | ''' 2 | [1] Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, Xiao-Li Li, "Neighborhood Regularized Logistic Matrix Factorization for Drug-target Interaction Prediction", under review. 3 | ''' 4 | import numpy as np 5 | from sklearn.metrics import precision_recall_curve, roc_curve 6 | from sklearn.metrics import auc 7 | 8 | 9 | class NRLMF: 10 | 11 | def __init__(self, cfix=5, K1=5, K2=5, num_factors=10, theta=1.0, lambda_d=0.625, lambda_t=0.625, alpha=0.1, beta=0.1, max_iter=100): 12 | self.cfix = int(cfix) # importance level for positive observations 13 | self.K1 = int(K1) 14 | self.K2 = int(K2) 15 | self.num_factors = int(num_factors) 16 | self.theta = float(theta) 17 | self.lambda_d = float(lambda_d) 18 | self.lambda_t = float(lambda_t) 19 | self.alpha = float(alpha) 20 | self.beta = float(beta) 21 | self.max_iter = int(max_iter) 22 | 23 | def AGD_optimization(self, seed=None): 24 | if seed is None: 25 | self.U = np.sqrt(1/float(self.num_factors))*np.random.normal(size=(self.num_drugs, self.num_factors)) 26 | self.V = np.sqrt(1/float(self.num_factors))*np.random.normal(size=(self.num_targets, self.num_factors)) 27 | else: 28 | prng = np.random.RandomState(seed) 29 | self.U = np.sqrt(1/float(self.num_factors))*prng.normal(size=(self.num_drugs, self.num_factors)) 30 | self.V = np.sqrt(1/float(self.num_factors))*prng.normal(size=(self.num_targets, self.num_factors)) 31 | dg_sum = np.zeros((self.num_drugs, self.U.shape[1])) 32 | tg_sum = np.zeros((self.num_targets, self.V.shape[1])) 33 | last_log = self.log_likelihood() 34 | for t in range(self.max_iter): 35 | dg = self.deriv(True) 36 | dg_sum += np.square(dg) 37 | vec_step_size = self.theta / np.sqrt(dg_sum) 38 | self.U += vec_step_size * dg 39 | tg = self.deriv(False) 40 | tg_sum += np.square(tg) 41 | vec_step_size = self.theta / np.sqrt(tg_sum) 42 | self.V += vec_step_size * tg 43 | curr_log = self.log_likelihood() 44 | delta_log = (curr_log-last_log)/abs(last_log) 45 | if abs(delta_log) < 1e-5: 46 | break 47 | last_log = curr_log 48 | 49 | def deriv(self, drug): 50 | if drug: 51 | vec_deriv = np.dot(self.intMat, self.V) 52 | else: 53 | vec_deriv = np.dot(self.intMat.T, self.U) 54 | A = np.dot(self.U, self.V.T) 55 | A = np.exp(A) 56 | A /= (A + self.ones) 57 | A = self.intMat1 * A 58 | if drug: 59 | vec_deriv -= np.dot(A, self.V) 60 | vec_deriv -= self.lambda_d*self.U+self.alpha*np.dot(self.DL, self.U) 61 | else: 62 | vec_deriv -= np.dot(A.T, self.U) 63 | vec_deriv -= self.lambda_t*self.V+self.beta*np.dot(self.TL, self.V) 64 | return vec_deriv 65 | 66 | def log_likelihood(self): 67 | loglik = 0 68 | A = np.dot(self.U, self.V.T) 69 | B = A * self.intMat 70 | loglik += np.sum(B) 71 | A = np.exp(A) 72 | A += self.ones 73 | A = np.log(A) 74 | A = self.intMat1 * A 75 | loglik -= np.sum(A) 76 | loglik -= 0.5 * self.lambda_d * np.sum(np.square(self.U))+0.5 * self.lambda_t * np.sum(np.square(self.V)) 77 | loglik -= 0.5 * self.alpha * np.sum(np.diag((np.dot(self.U.T, self.DL)).dot(self.U))) 78 | loglik -= 0.5 * self.beta * np.sum(np.diag((np.dot(self.V.T, self.TL)).dot(self.V))) 79 | return loglik 80 | 81 | def construct_neighborhood(self, drugMat, targetMat): 82 | self.dsMat = drugMat - np.diag(np.diag(drugMat)) 83 | self.tsMat = targetMat - np.diag(np.diag(targetMat)) 84 | if self.K1 > 0: 85 | S1 = self.get_nearest_neighbors(self.dsMat, self.K1) 86 | self.DL = self.laplacian_matrix(S1) 87 | S2 = self.get_nearest_neighbors(self.tsMat, self.K1) 88 | self.TL = self.laplacian_matrix(S2) 89 | else: 90 | self.DL = self.laplacian_matrix(self.dsMat) 91 | self.TL = self.laplacian_matrix(self.tsMat) 92 | 93 | def laplacian_matrix(self, S): 94 | x = np.sum(S, axis=0) 95 | y = np.sum(S, axis=1) 96 | L = 0.5*(np.diag(x+y) - (S+S.T)) # neighborhood regularization matrix 97 | return L 98 | 99 | def get_nearest_neighbors(self, S, size=5): 100 | m, n = S.shape 101 | X = np.zeros((m, n)) 102 | for i in xrange(m): 103 | ii = np.argsort(S[i, :])[::-1][:min(size, n)] 104 | X[i, ii] = S[i, ii] 105 | return X 106 | 107 | def fix_model(self, W, intMat, drugMat, targetMat, seed=None): 108 | self.num_drugs, self.num_targets = intMat.shape 109 | self.ones = np.ones((self.num_drugs, self.num_targets)) 110 | self.intMat = self.cfix*intMat*W 111 | self.intMat1 = (self.cfix-1)*intMat*W + self.ones 112 | x, y = np.where(self.intMat > 0) 113 | self.train_drugs, self.train_targets = set(x.tolist()), set(y.tolist()) 114 | self.construct_neighborhood(drugMat, targetMat) 115 | self.AGD_optimization(seed) 116 | 117 | def predict_scores(self, test_data, N): 118 | dinx = np.array(list(self.train_drugs)) 119 | DS = self.dsMat[:, dinx] 120 | tinx = np.array(list(self.train_targets)) 121 | TS = self.tsMat[:, tinx] 122 | scores = [] 123 | for d, t in test_data: 124 | if d in self.train_drugs: 125 | if t in self.train_targets: 126 | val = np.sum(self.U[d, :]*self.V[t, :]) 127 | else: 128 | jj = np.argsort(TS[t, :])[::-1][:N] 129 | val = np.sum(self.U[d, :]*np.dot(TS[t, jj], self.V[tinx[jj], :]))/np.sum(TS[t, jj]) 130 | else: 131 | if t in self.train_targets: 132 | ii = np.argsort(DS[d, :])[::-1][:N] 133 | val = np.sum(np.dot(DS[d, ii], self.U[dinx[ii], :])*self.V[t, :])/np.sum(DS[d, ii]) 134 | else: 135 | ii = np.argsort(DS[d, :])[::-1][:N] 136 | jj = np.argsort(TS[t, :])[::-1][:N] 137 | v1 = DS[d, ii].dot(self.U[dinx[ii], :])/np.sum(DS[d, ii]) 138 | v2 = TS[t, jj].dot(self.V[tinx[jj], :])/np.sum(TS[t, jj]) 139 | val = np.sum(v1*v2) 140 | scores.append(np.exp(val)/(1+np.exp(val))) 141 | return np.array(scores) 142 | 143 | def evaluation(self, test_data, test_label): 144 | dinx = np.array(list(self.train_drugs)) 145 | DS = self.dsMat[:, dinx] 146 | tinx = np.array(list(self.train_targets)) 147 | TS = self.tsMat[:, tinx] 148 | scores = [] 149 | if self.K2 > 0: 150 | for d, t in test_data: 151 | if d in self.train_drugs: 152 | if t in self.train_targets: 153 | val = np.sum(self.U[d, :]*self.V[t, :]) 154 | else: 155 | jj = np.argsort(TS[t, :])[::-1][:self.K2] 156 | val = np.sum(self.U[d, :]*np.dot(TS[t, jj], self.V[tinx[jj], :]))/np.sum(TS[t, jj]) 157 | else: 158 | if t in self.train_targets: 159 | ii = np.argsort(DS[d, :])[::-1][:self.K2] 160 | val = np.sum(np.dot(DS[d, ii], self.U[dinx[ii], :])*self.V[t, :])/np.sum(DS[d, ii]) 161 | else: 162 | ii = np.argsort(DS[d, :])[::-1][:self.K2] 163 | jj = np.argsort(TS[t, :])[::-1][:self.K2] 164 | v1 = DS[d, ii].dot(self.U[dinx[ii], :])/np.sum(DS[d, ii]) 165 | v2 = TS[t, jj].dot(self.V[tinx[jj], :])/np.sum(TS[t, jj]) 166 | val = np.sum(v1*v2) 167 | scores.append(np.exp(val)/(1+np.exp(val))) 168 | elif self.K2 == 0: 169 | for d, t in test_data: 170 | val = np.sum(self.U[d, :]*self.V[t, :]) 171 | scores.append(np.exp(val)/(1+np.exp(val))) 172 | prec, rec, thr = precision_recall_curve(test_label, np.array(scores)) 173 | aupr_val = auc(rec, prec) 174 | fpr, tpr, thr = roc_curve(test_label, np.array(scores)) 175 | auc_val = auc(fpr, tpr) 176 | return aupr_val, auc_val 177 | 178 | def __str__(self): 179 | return "Model: NRLMF, c:%s, K1:%s, K2:%s, r:%s, lambda_d:%s, lambda_t:%s, alpha:%s, beta:%s, theta:%s, max_iter:%s" % (self.cfix, self.K1, self.K2, self.num_factors, self.lambda_d, self.lambda_t, self.alpha, self.beta, self.theta, self.max_iter) 180 | -------------------------------------------------------------------------------- /sat_analysis.py: -------------------------------------------------------------------------------- 1 | 2 | from functions import * 3 | import scipy.stats as st 4 | 5 | 6 | for cv in ["1", "2", "3"]: 7 | print "CVS:"+cv 8 | for dataset in ["nr", "gpcr", "ic", "e"]: 9 | nrlmf_auc = load_metric_vector("../output/logmf_auc_"+cv+"_"+dataset+".txt") 10 | nrlmf_aupr = load_metric_vector("../output/logmf_aupr_"+cv+"_"+dataset+".txt") 11 | for cp in ["netlaprls", "blm", "wnnrls", "kbmf", "cmf"]: 12 | cp_auc = load_metric_vector("../output/"+cp+"_auc_"+cv+"_"+dataset+".txt") 13 | cp_aupr = load_metric_vector("../output/"+cp+"_aupr_"+cv+"_"+dataset+".txt") 14 | x1, y1 = st.ttest_ind(nrlmf_auc, cp_auc) 15 | x2, y2 = st.ttest_ind(nrlmf_aupr, cp_aupr) 16 | print dataset, cp, x1, y1, x2, y2 17 | print "" 18 | -------------------------------------------------------------------------------- /wnngip.py: -------------------------------------------------------------------------------- 1 | 2 | ''' 3 | [1] van Laarhoven, Twan, Sander B. Nabuurs, and Elena Marchiori. "Gaussian interaction profile kernels for predicting drug-target interaction." Bioinformatics 27.21 (2011): 3036-3043. 4 | [2] van Laarhoven, Twan, and Elena Marchiori. "Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile." PloS one 8.6 (2013): e66952. 5 | 6 | Default Parameters: 7 | T = 0.7 (the parameter T in Section [2]) 8 | sigma = 1.0 9 | alpha = 0.5 10 | gamma = 1.0 11 | ''' 12 | import numpy as np 13 | from sklearn.metrics.pairwise import rbf_kernel 14 | from sklearn.metrics import precision_recall_curve, roc_curve 15 | from sklearn.metrics import auc 16 | 17 | 18 | class WNNGIP: 19 | 20 | def __init__(self, T=0.7, sigma=1, alpha=0.5, gamma=1.0): 21 | self.T = T # the decay parameter 22 | self.sigma = sigma # the regularization parameter 23 | self.alpha = alpha # the weight parameter used in combining different kernels 24 | self.gamma = gamma # the bandwidth of the GIP kernel 25 | 26 | def preprocess_wnn(self, R, S, train_inx, new_inx, drug=True): 27 | for d in new_inx: 28 | ii = np.argsort(S[d, train_inx])[::-1] 29 | inx = train_inx[ii] 30 | for i in xrange(inx.size): 31 | w = self.T**(i) 32 | if w >= 1e-4: 33 | if drug: 34 | R[d, :] += w*R[inx[i], :] 35 | else: 36 | R[:, d] += w*R[:, inx[i]] 37 | else: 38 | break 39 | 40 | def rls_kron_train(self, R, Kd, Kt): 41 | m, n = R.shape 42 | ld, vd = np.linalg.eig(Kd) 43 | lt, vt = np.linalg.eig(Kt) 44 | vec = ld.reshape((ld.size, 1))*lt.reshape((1, lt.size)) 45 | vec = vec.reshape((1, vec.size)) 46 | x = vec*(1.0/(vec+self.sigma)) 47 | y = np.dot(np.dot(vt.T, R.T), vd) 48 | y = y.reshape((1, y.size)) 49 | z = (x*y).reshape((n, m)) # need to check 50 | self.predictR = np.dot(np.dot(vd, z.T), vt.T) 51 | 52 | def kernel_combination(self, R, S, new_inx, bandwidth): 53 | K = self.alpha*S+(1.0-self.alpha)*rbf_kernel(R, gamma=bandwidth) 54 | K[new_inx, :] = S[new_inx, :] 55 | K[:, new_inx] = S[:, new_inx] 56 | return K 57 | 58 | def fix_model(self, W, intMat, drugMat, targetMat, seed=None, epsilon=0.1): 59 | R = W*intMat 60 | m, n = intMat.shape 61 | x, y = np.where(R > 0) 62 | # Enforce the positive definite property of similarity matrix 63 | drugMat = (drugMat+drugMat.T)/2 + epsilon*np.eye(m) 64 | targetMat = (targetMat+targetMat.T)/2 + epsilon*np.eye(n) 65 | train_drugs = np.array(list(set(x.tolist())), dtype=np.int32) 66 | train_targets = np.array(list(set(y.tolist())), dtype=np.int32) 67 | new_drugs = np.array(list(set(xrange(m)) - set(x.tolist())), dtype=np.int32) 68 | new_targets = np.array(list(set(xrange(n)) - set(y.tolist())), dtype=np.int32) 69 | drug_bw = self.gamma*m/len(x) 70 | target_bw = self.gamma*n/len(x) 71 | Kd = self.kernel_combination(R, drugMat, new_drugs, drug_bw) 72 | Kt = self.kernel_combination(R.T, targetMat, new_targets, target_bw) 73 | self.preprocess_wnn(R, drugMat, train_drugs, new_drugs, True) 74 | self.preprocess_wnn(R, targetMat, train_targets, new_targets, False) 75 | self.rls_kron_train(R, Kd, Kt) 76 | 77 | def predict_scores(self, test_data, N): 78 | inx = np.array(test_data) 79 | return self.predictR[inx[:, 0], inx[:, 1]] 80 | 81 | def evaluation(self, test_data, test_label): 82 | scores = self.predictR[test_data[:, 0], test_data[:, 1]] 83 | prec, rec, thr = precision_recall_curve(test_label, scores) 84 | aupr_val = auc(rec, prec) 85 | fpr, tpr, thr = roc_curve(test_label, scores) 86 | auc_val = auc(fpr, tpr) 87 | return aupr_val, auc_val 88 | 89 | def __str__(self): 90 | return "Model: RLSWNN, T:%s, sigma:%s, alpha:%s, gamma:%s" % (self.T, self.sigma, self.alpha, self.gamma) 91 | --------------------------------------------------------------------------------